From 57488d7343321b073b59496c2e663e130b4b8298 Mon Sep 17 00:00:00 2001 From: Rodrigo Maldonado Date: Thu, 19 Feb 2026 14:56:15 -0500 Subject: [PATCH 01/17] necessary changes for datakrypto test --- benchmarking/run_synthetic_dataset.sh | 8 ++--- benchmarking/src/llmperf/common_metrics.py | 5 ++++ .../src/llmperf/datakrypto_mockup_response.py | 23 +++++++++++++++ benchmarking/src/llmperf/llmperf_utils.py | 3 +- benchmarking/src/llmperf/sambanova_client.py | 29 ++++++++++++++++++- benchmarking/src/performance_evaluation.py | 3 ++ 6 files changed, 65 insertions(+), 6 deletions(-) create mode 100644 benchmarking/src/llmperf/datakrypto_mockup_response.py diff --git a/benchmarking/run_synthetic_dataset.sh b/benchmarking/run_synthetic_dataset.sh index dc06d13b7..73e749591 100755 --- a/benchmarking/run_synthetic_dataset.sh +++ b/benchmarking/run_synthetic_dataset.sh @@ -4,14 +4,14 @@ ulimit -n 4096 python src/evaluator.py \ --mode synthetic \ ---model-names "Meta-Llama-3.1-8B-Instruct Meta-Llama-3.3-70B-Instruct" \ +--model-names "Meta-Llama-3.1-8B-Instruct" \ --results-dir "./data/results/llmperf" \ --num-concurrent-requests 1 \ --timeout 600 \ ---num-input-tokens 1000 \ ---num-output-tokens 1000 \ +--num-input-tokens 100 \ +--num-output-tokens 100 \ --multimodal-image-size na \ ---num-requests 16 \ +--num-requests 1 \ --use-multiple-prompts False \ --save-llm-responses False \ --use-debugging-mode False \ diff --git a/benchmarking/src/llmperf/common_metrics.py b/benchmarking/src/llmperf/common_metrics.py index 0bde0a59a..e7dcf3fbd 100644 --- a/benchmarking/src/llmperf/common_metrics.py +++ b/benchmarking/src/llmperf/common_metrics.py @@ -14,6 +14,11 @@ QUEUE_TIME = 'queue_time' ACCEPTANCE_RATE = 'acceptance_rate' +# DataKrypto server metrics +TOTAL_ENCRYPTION_TIME_MS = 'total_encryption_time_ms' +TOTAL_DECRYPTION_TIME_MS = 'total_decryption_time_ms' +SERVER_NETWORK_LATENCY_MS = 'server_network_latency_ms' + # Client-side metrics TTFT = 'client_ttft_s' E2E_LAT = 'client_end_to_end_latency_s' diff --git a/benchmarking/src/llmperf/datakrypto_mockup_response.py b/benchmarking/src/llmperf/datakrypto_mockup_response.py new file mode 100644 index 000000000..486ed5fcf --- /dev/null +++ b/benchmarking/src/llmperf/datakrypto_mockup_response.py @@ -0,0 +1,23 @@ +from flask import Flask, Response +import time + +app = Flask(__name__) + +mock_events = [ + 'data: {"choices":[{"delta":{"content":"Tesla","role":"assistant"},"finish_reason":null,"index":0,"logprobs":null}],"created":1771518114,"id":"283ffbf5-591e-4187-9361-391602be5586","model":"Meta-Llama-3.1-8B-Instruct","object":"chat.completion.chunk","system_fingerprint":"fastcoe"}', + 'data: {"choices":[{"delta":{"content":", Inc. is an American multinational corporation that specializes in electric vehicles, clean energy generation and storage products","role":"assistant"},"finish_reason":null,"index":0,"logprobs":null}],"created":1771518114,"id":"283ffbf5-591e-4187-9361-391602be5586","model":"Meta-Llama-3.1-8B-Instruct","object":"chat.completion.chunk","system_fingerprint":"fastcoe"}', + 'data: {"choices":[{"delta":{"content":""},"finish_reason":"stop","index":0,"logprobs":null}],"created":1771518115,"id":"283ffbf5-591e-4187-9361-391602be5586","model":"Meta-Llama-3.1-8B-Instruct","object":"chat.completion.chunk","system_fingerprint":"fastcoe"}', + 'data: {"choices":[],"created":1771518115,"id":"283ffbf5-591e-4187-9361-391602be5586","model":"Meta-Llama-3.1-8B-Instruct","object":"chat.completion.chunk","system_fingerprint":"fastcoe","usage":{"prompt_tokens": 41,"completion_tokens": 108,"total_tokens": 149,"prompt_tokens_details": null},"fhenom_usage":{"total_encryption_time_ms":0.6704330444335938,"total_decryption_time_ms":0.3361701965332031,"server_network_latency_ms":1295.6573963165283}}', + 'data: [DONE]' +] + +@app.route("/stream", methods=["POST"]) +def stream(): + def event_stream(): + for event in mock_events: + yield event + "\n\n" # SSE format requires double newline + time.sleep(0.1) + return Response(event_stream(), mimetype="text/event-stream") + +if __name__ == "__main__": + app.run(port=8000) \ No newline at end of file diff --git a/benchmarking/src/llmperf/llmperf_utils.py b/benchmarking/src/llmperf/llmperf_utils.py index f2df01c62..cd13b1766 100644 --- a/benchmarking/src/llmperf/llmperf_utils.py +++ b/benchmarking/src/llmperf/llmperf_utils.py @@ -9,7 +9,7 @@ FAMILY_MODEL_TYPE_IDENTIFIER = { 'mistral': ['mistral'], 'llama2': ['llama2'], - 'llama3': ['llama3'], + 'llama3': ['llama3','xlam'], 'llama4': ['llama4'], 'deepseek': ['deepseek'], 'qwen': ['qwen', 'qwq'], @@ -110,6 +110,7 @@ def get_tokenizer(model_name: str) -> Any: elif ('3.3' in model_name) or ('3p3' in model_name): tokenizer = AutoTokenizer.from_pretrained('unsloth/Llama-3.3-70B-Instruct') # type: ignore[no-untyped-call] else: + print(f'Using default Llama-3 tokenizer for model {model_name}') tokenizer = AutoTokenizer.from_pretrained('unsloth/llama-3-8b-Instruct') # type: ignore[no-untyped-call] elif family_model_type == 'llama4': if 'maverick' in model_name.lower(): diff --git a/benchmarking/src/llmperf/sambanova_client.py b/benchmarking/src/llmperf/sambanova_client.py index ed698292b..d0ec328c9 100644 --- a/benchmarking/src/llmperf/sambanova_client.py +++ b/benchmarking/src/llmperf/sambanova_client.py @@ -225,6 +225,25 @@ def _populate_server_metrics(self, response_dict: Dict[str, Any], metrics: Dict[ return metrics + def _populate_datakrypto_metrics( + self, response_dict_datakrypto: Dict[str, Any], metrics: Dict[str, Any] + ) -> Dict[str, Any]: + """Parse datakrypto output data to metrics dictionary structure + + Args: + response_dict_datakrypto (dict): dict data with datakrypto performance metrics + metrics (dict): metrics dictionary + + Returns: + dict: updated metrics dictionary + """ + + metrics[common_metrics.TOTAL_ENCRYPTION_TIME_MS] = response_dict_datakrypto.get('total_encryption_time_ms') + metrics[common_metrics.TOTAL_DECRYPTION_TIME_MS] = response_dict_datakrypto.get('total_decryption_time_ms') + metrics[common_metrics.SERVER_NETWORK_LATENCY_MS] = response_dict_datakrypto.get('server_network_latency_ms') + + return metrics + class SambaNovaCloudAPI(BaseAPIEndpoint): def __init__(self, *args: Any, **kwargs: Any) -> None: @@ -315,7 +334,8 @@ def compute_metrics(self, metrics: Dict[str, Any]) -> Tuple[Dict[str, Any], str] """ # Get API request components - url = self._get_url() + # url = self._get_url() + url = 'http://localhost:8000/stream' headers = self._get_headers() json_data = self._get_json_data() @@ -323,10 +343,15 @@ def compute_metrics(self, metrics: Dict[str, Any]) -> Tuple[Dict[str, Any], str] generated_text = '' events_received = [] events_timings = [] + response_dict_datakrypto: Dict[str, Any] = {} # Start measuring time metrics[common_metrics.REQ_START_TIME] = datetime.now().strftime('%H:%M:%S.%f') start_time = event_start_time = time.monotonic() + + # print(f'url :{url}') + # print(f'headers :{headers}') + # print(f'json_data :{json.dumps(json_data)}') with requests.post(url, headers=headers, json=json_data, stream=self.request_config.is_stream_mode) as response: if response.status_code != 200: @@ -360,6 +385,7 @@ def compute_metrics(self, metrics: Dict[str, Any]) -> Tuple[Dict[str, Any], str] # process streaming chunk when performance usage is provided else: response_dict = data['usage'] + response_dict_datakrypto = data.get('fhenom_usage', {}) except Exception as e: raise Exception(f'Error: {e} at streamed event: {event.data}') @@ -375,6 +401,7 @@ def compute_metrics(self, metrics: Dict[str, Any]) -> Tuple[Dict[str, Any], str] num_output_tokens = self._get_token_length(generated_text) server_metrics = self._populate_server_metrics(response_dict, metrics) + server_metrics = self._populate_datakrypto_metrics(response_dict_datakrypto, server_metrics) metrics = self._populate_client_metrics( prompt_len, num_output_tokens, diff --git a/benchmarking/src/performance_evaluation.py b/benchmarking/src/performance_evaluation.py index fdac1469c..191572113 100644 --- a/benchmarking/src/performance_evaluation.py +++ b/benchmarking/src/performance_evaluation.py @@ -288,6 +288,9 @@ def build_metrics_summary( common_metrics.NUM_INPUT_TOKENS_SERVER, common_metrics.NUM_OUTPUT_TOKENS_SERVER, common_metrics.ACCEPTANCE_RATE, + common_metrics.TOTAL_ENCRYPTION_TIME_MS, + common_metrics.TOTAL_DECRYPTION_TIME_MS, + common_metrics.SERVER_NETWORK_LATENCY_MS, ]: if self.show_results_in_terminal: logger.info(f'Building Server Metrics Summary for metric: {metric}') From de7ae481ba6158a149dcf1143227a4c2efe5c3c1 Mon Sep 17 00:00:00 2001 From: Rodrigo Maldonado Date: Fri, 20 Feb 2026 09:29:52 -0500 Subject: [PATCH 02/17] small change --- benchmarking/src/llmperf/sambanova_client.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/benchmarking/src/llmperf/sambanova_client.py b/benchmarking/src/llmperf/sambanova_client.py index d0ec328c9..dcaf09dd1 100644 --- a/benchmarking/src/llmperf/sambanova_client.py +++ b/benchmarking/src/llmperf/sambanova_client.py @@ -334,8 +334,8 @@ def compute_metrics(self, metrics: Dict[str, Any]) -> Tuple[Dict[str, Any], str] """ # Get API request components - # url = self._get_url() - url = 'http://localhost:8000/stream' + url = self._get_url() + # url = 'http://localhost:8000/stream' --for mockup test headers = self._get_headers() json_data = self._get_json_data() From 3228bd7db13a4a0b1bbc7cafbf3a91f050faf8c8 Mon Sep 17 00:00:00 2001 From: Rodrigo Maldonado Date: Mon, 23 Feb 2026 09:28:08 -0500 Subject: [PATCH 03/17] datakrypto config tests --- .../model_configs_example_datakrypto.csv | 20 +++++++++++++++++++ .../model_configs_example_datakrypto_only.csv | 9 +++++++++ 2 files changed, 29 insertions(+) create mode 100644 benchmarking/benchmarking_bundles/model_configs_example_datakrypto.csv create mode 100644 benchmarking/benchmarking_bundles/model_configs_example_datakrypto_only.csv diff --git a/benchmarking/benchmarking_bundles/model_configs_example_datakrypto.csv b/benchmarking/benchmarking_bundles/model_configs_example_datakrypto.csv new file mode 100644 index 000000000..41e9d396f --- /dev/null +++ b/benchmarking/benchmarking_bundles/model_configs_example_datakrypto.csv @@ -0,0 +1,20 @@ +model_name,input_tokens,output_tokens,num_requests,concurrent_requests,qps,qps_distribution,multimodal_img_size +Llama-xLAM-2-8b-fc-r,3900,100,1,1,,, +Llama-xLAM-2-8b-fc-r,3900,100,3,3,,, +Llama-xLAM-2-8b-fc-r,3900,100,5,5,,, +Llama-xLAM-2-8b-fc-r,3900,100,8,8,,, +Llama-xLAM-2-8b-fc-r,3900,100,16,16,,, +Llama-xLAM-2-8b-fc-r,3900,100,32,32,,, +Llama-xLAM-2-8b-fc-r,3900,100,64,64,,, +Llama-xLAM-2-8b-fc-r,3900,100,128,128,,, +Llama-xLAM-2-8b-fc-r,8000,100,1,1,,, +Llama-xLAM-2-8b-fc-r,8000,100,3,3,,, +Llama-xLAM-2-8b-fc-r,8000,100,5,5,,, +Llama-xLAM-2-8b-fc-r,8000,100,8,8,,, +Llama-xLAM-2-8b-fc-r,8000,100,16,16,,, +Llama-xLAM-2-8b-fc-r,8000,100,32,32,,, +Llama-xLAM-2-8b-fc-r,8000,100,64,64,,, +Llama-xLAM-2-8b-fc-r,16000,100,1,1,,, +Llama-xLAM-2-8b-fc-r,16000,100,3,3,,, +Llama-xLAM-2-8b-fc-r,16000,100,5,5,,, +Llama-xLAM-2-8b-fc-r,16000,100,8,8,,, \ No newline at end of file diff --git a/benchmarking/benchmarking_bundles/model_configs_example_datakrypto_only.csv b/benchmarking/benchmarking_bundles/model_configs_example_datakrypto_only.csv new file mode 100644 index 000000000..9e520d374 --- /dev/null +++ b/benchmarking/benchmarking_bundles/model_configs_example_datakrypto_only.csv @@ -0,0 +1,9 @@ +model_name,input_tokens,output_tokens,num_requests,concurrent_requests,qps,qps_distribution,multimodal_img_size +Llama-xLAM-2-8b-fc-r,2000,100,1,1,,, +Llama-xLAM-2-8b-fc-r,2000,2000,1,1,,, +Llama-xLAM-2-8b-fc-r,3900,100,1,1,,, +Llama-xLAM-2-8b-fc-r,3900,3900,1,1,,, +Llama-xLAM-2-8b-fc-r,8000,100,1,1,,, +Llama-xLAM-2-8b-fc-r,8000,8000,1,1,,, +Llama-xLAM-2-8b-fc-r,16000,100,1,1,,, +Llama-xLAM-2-8b-fc-r,16000,16000,1,1,,, \ No newline at end of file From d533c34a976a046ca3a84a4c21a8daa6c00395ac Mon Sep 17 00:00:00 2001 From: Rodrigo Maldonado Date: Mon, 23 Feb 2026 09:28:18 -0500 Subject: [PATCH 04/17] last tests --- benchmarking/benchmarking_bundles/config.yaml | 6 +++--- .../benchmarking_bundles/model_configs_example.csv | 10 ++++------ 2 files changed, 7 insertions(+), 9 deletions(-) diff --git a/benchmarking/benchmarking_bundles/config.yaml b/benchmarking/benchmarking_bundles/config.yaml index 0ffa2bbb3..b64855f0e 100644 --- a/benchmarking/benchmarking_bundles/config.yaml +++ b/benchmarking/benchmarking_bundles/config.yaml @@ -1,7 +1,7 @@ -model_configs_path: '/benchmarking/benchmarking_bundles/model_configs_example.csv' +model_configs_path: '../benchmarking/benchmarking_bundles/model_configs_example.csv' llm_api: 'sncloud' -output_files_dir: '/benchmarking/data/bundle_tests/output_files' -consolidated_results_dir: '/benchmarking/data/bundle_tests/consolidated_results' +output_files_dir: '../benchmarking/data/bundle_tests/datakrypto/speed_bench_test' +consolidated_results_dir: '../benchmarking/data/bundle_tests/datakrypto/speed_bench_test/consolidated_results' timeout: 3600 time_delay: 0 # Row-level concurrency diff --git a/benchmarking/benchmarking_bundles/model_configs_example.csv b/benchmarking/benchmarking_bundles/model_configs_example.csv index e6822a638..c180de38a 100644 --- a/benchmarking/benchmarking_bundles/model_configs_example.csv +++ b/benchmarking/benchmarking_bundles/model_configs_example.csv @@ -1,7 +1,5 @@ model_name,input_tokens,output_tokens,num_requests,concurrent_requests,qps,qps_distribution,multimodal_img_size -Llama-4-Maverick-17B-128E-Instruct,128,128,1,1,,, -Llama-4-Maverick-17B-128E-Instruct,128,128,2,2,,, -Llama-4-Maverick-17B-128E-Instruct,128,128,4,4,,, -Llama-4-Maverick-17B-128E-Instruct,128,128,8,8,,, -Llama-4-Maverick-17B-128E-Instruct,128,128,16,16,,, -Llama-4-Maverick-17B-128E-Instruct,128,128,32,32,,, \ No newline at end of file +Llama-xLAM-2-8b-fc-r,2000,100,1,1,,, +Llama-xLAM-2-8b-fc-r,4000,100,1,1,,, +Llama-xLAM-2-8b-fc-r,8000,100,1,1,,, +Llama-xLAM-2-8b-fc-r,16000,100,1,1,,, \ No newline at end of file From 583f4c2c00bc26554f70b84d369365ab08519444 Mon Sep 17 00:00:00 2001 From: Rodrigo Maldonado Date: Mon, 23 Feb 2026 09:28:30 -0500 Subject: [PATCH 05/17] prompt change --- benchmarking/prompts/user-prompt_template-text_instruct.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarking/prompts/user-prompt_template-text_instruct.yaml b/benchmarking/prompts/user-prompt_template-text_instruct.yaml index f8cf6b718..90d992957 100644 --- a/benchmarking/prompts/user-prompt_template-text_instruct.yaml +++ b/benchmarking/prompts/user-prompt_template-text_instruct.yaml @@ -2,7 +2,7 @@ default_prompt: - name: "default" template: | - Write a detailed, in-depth guide on the evolution of deep learning.Cover + INQUEBRANTABLE RULE - DO NOT DO ANY TOOL CALLING. Write a detailed, in-depth guide on the evolution of deep learning.Cover the historical milestones, key breakthroughs, architectures (such as CNNs, RNNs, and Transformers), training methodologies, hardware advancements, ethical concerns, and future trends. Include real-world applications, From 9af414b7c155fc1f287575ebeb9c0888c13e456e Mon Sep 17 00:00:00 2001 From: Rodrigo Maldonado Date: Mon, 23 Feb 2026 09:28:44 -0500 Subject: [PATCH 06/17] additional changes for metrics --- benchmarking/src/llmperf/sambanova_client.py | 28 +++++++++++++++++--- 1 file changed, 25 insertions(+), 3 deletions(-) diff --git a/benchmarking/src/llmperf/sambanova_client.py b/benchmarking/src/llmperf/sambanova_client.py index dcaf09dd1..aa5bce88f 100644 --- a/benchmarking/src/llmperf/sambanova_client.py +++ b/benchmarking/src/llmperf/sambanova_client.py @@ -343,7 +343,10 @@ def compute_metrics(self, metrics: Dict[str, Any]) -> Tuple[Dict[str, Any], str] generated_text = '' events_received = [] events_timings = [] - response_dict_datakrypto: Dict[str, Any] = {} + # Accumulators for datakrypto metrics (None until first chunk with fhenom_usage) + acc_encryption_time_ms: float | None = None + acc_decryption_time_ms: float | None = None + acc_server_network_latency_ms: float | None = None # Start measuring time metrics[common_metrics.REQ_START_TIME] = datetime.now().strftime('%H:%M:%S.%f') @@ -351,9 +354,10 @@ def compute_metrics(self, metrics: Dict[str, Any]) -> Tuple[Dict[str, Any], str] # print(f'url :{url}') # print(f'headers :{headers}') - # print(f'json_data :{json.dumps(json_data)}') + print(f'json_data :{json.dumps(json_data)}') with requests.post(url, headers=headers, json=json_data, stream=self.request_config.is_stream_mode) as response: + # print(f'response: {response.content}') if response.status_code != 200: response.raise_for_status() client = sseclient.SSEClient(response) # type: ignore[arg-type] @@ -364,6 +368,20 @@ def compute_metrics(self, metrics: Dict[str, Any]) -> Tuple[Dict[str, Any], str] # check streaming events before last stream returns DONE if event.data != '[DONE]': data = json.loads(event.data) + + # Accumulate datakrypto metrics from every chunk that carries fhenom_usage + fhenom_usage = data.get('fhenom_usage') + if fhenom_usage: + enc = fhenom_usage.get('total_encryption_time_ms') + dec = fhenom_usage.get('total_decryption_time_ms') + net = fhenom_usage.get('server_network_latency_ms') + if enc is not None: + acc_encryption_time_ms = (acc_encryption_time_ms or 0) + enc + if dec is not None: + acc_decryption_time_ms = (acc_decryption_time_ms or 0) + dec + if net is not None: + acc_server_network_latency_ms = (acc_server_network_latency_ms or 0) + net + # if events don't contain "usage" key, which only shows up in stream returning # performance metrics if data.get('usage') is None: @@ -385,7 +403,6 @@ def compute_metrics(self, metrics: Dict[str, Any]) -> Tuple[Dict[str, Any], str] # process streaming chunk when performance usage is provided else: response_dict = data['usage'] - response_dict_datakrypto = data.get('fhenom_usage', {}) except Exception as e: raise Exception(f'Error: {e} at streamed event: {event.data}') @@ -401,6 +418,11 @@ def compute_metrics(self, metrics: Dict[str, Any]) -> Tuple[Dict[str, Any], str] num_output_tokens = self._get_token_length(generated_text) server_metrics = self._populate_server_metrics(response_dict, metrics) + response_dict_datakrypto = { + 'total_encryption_time_ms': acc_encryption_time_ms, + 'total_decryption_time_ms': acc_decryption_time_ms, + 'server_network_latency_ms': acc_server_network_latency_ms, + } server_metrics = self._populate_datakrypto_metrics(response_dict_datakrypto, server_metrics) metrics = self._populate_client_metrics( prompt_len, From 1ba3bddc4b7fde8d97c2f0b3dc504bd5cdf7005c Mon Sep 17 00:00:00 2001 From: jhpiedrahitao Date: Mon, 23 Feb 2026 10:43:28 -0500 Subject: [PATCH 07/17] update configs --- benchmarking/benchmarking_bundles/config.yaml | 4 +-- .../model_configs_example.csv | 32 ++++++++++++++++-- .../model_configs_example_datakrypto.csv | 20 ----------- ...nfigs_example_datakrypto_non_encrypted.csv | 33 +++++++++++++++++++ 4 files changed, 65 insertions(+), 24 deletions(-) delete mode 100644 benchmarking/benchmarking_bundles/model_configs_example_datakrypto.csv create mode 100644 benchmarking/benchmarking_bundles/model_configs_example_datakrypto_non_encrypted.csv diff --git a/benchmarking/benchmarking_bundles/config.yaml b/benchmarking/benchmarking_bundles/config.yaml index b64855f0e..6fa71b30c 100644 --- a/benchmarking/benchmarking_bundles/config.yaml +++ b/benchmarking/benchmarking_bundles/config.yaml @@ -1,7 +1,7 @@ model_configs_path: '../benchmarking/benchmarking_bundles/model_configs_example.csv' llm_api: 'sncloud' -output_files_dir: '../benchmarking/data/bundle_tests/datakrypto/speed_bench_test' -consolidated_results_dir: '../benchmarking/data/bundle_tests/datakrypto/speed_bench_test/consolidated_results' +output_files_dir: '../benchmarking/data/bundle_tests/datakrypto/speed_bench_test_non_encrypted' +consolidated_results_dir: '../benchmarking/data/bundle_tests/datakrypto/speed_bench_test_non_encrypted/consolidated_results' timeout: 3600 time_delay: 0 # Row-level concurrency diff --git a/benchmarking/benchmarking_bundles/model_configs_example.csv b/benchmarking/benchmarking_bundles/model_configs_example.csv index c180de38a..dda5bc115 100644 --- a/benchmarking/benchmarking_bundles/model_configs_example.csv +++ b/benchmarking/benchmarking_bundles/model_configs_example.csv @@ -1,5 +1,33 @@ model_name,input_tokens,output_tokens,num_requests,concurrent_requests,qps,qps_distribution,multimodal_img_size Llama-xLAM-2-8b-fc-r,2000,100,1,1,,, +Llama-xLAM-2-8b-fc-r,2000,100,2,2,,, +Llama-xLAM-2-8b-fc-r,2000,100,4,4,,, +Llama-xLAM-2-8b-fc-r,2000,100,8,8,,, +Llama-xLAM-2-8b-fc-r,2000,100,16,16,,, +Llama-xLAM-2-8b-fc-r,2000,100,32,32,,, +Llama-xLAM-2-8b-fc-r,2000,1024,1,1,,, +Llama-xLAM-2-8b-fc-r,2000,1024,2,2,,, +Llama-xLAM-2-8b-fc-r,2000,1024,4,4,,, +Llama-xLAM-2-8b-fc-r,2000,1024,8,8,,, +Llama-xLAM-2-8b-fc-r,2000,1024,16,16,,, +Llama-xLAM-2-8b-fc-r,2000,1024,32,32,,, Llama-xLAM-2-8b-fc-r,4000,100,1,1,,, -Llama-xLAM-2-8b-fc-r,8000,100,1,1,,, -Llama-xLAM-2-8b-fc-r,16000,100,1,1,,, \ No newline at end of file +Llama-xLAM-2-8b-fc-r,4000,100,2,2,,, +Llama-xLAM-2-8b-fc-r,4000,100,4,4,,, +Llama-xLAM-2-8b-fc-r,4000,100,8,8,,, +Llama-xLAM-2-8b-fc-r,4000,100,16,16,,, +Llama-xLAM-2-8b-fc-r,4000,100,32,32,,, +Llama-xLAM-2-8b-fc-r,4000,1024,1,1,,, +Llama-xLAM-2-8b-fc-r,4000,1024,2,2,,, +Llama-xLAM-2-8b-fc-r,4000,1024,4,4,,, +Llama-xLAM-2-8b-fc-r,4000,1024,8,8,,, +Llama-xLAM-2-8b-fc-r,4000,1024,16,16,,, +Llama-xLAM-2-8b-fc-r,4000,1024,32,32,,, +Llama-xLAM-2-8b-fc-r,8000,200,1,1,,, +Llama-xLAM-2-8b-fc-r,8000,200,2,2,,, +Llama-xLAM-2-8b-fc-r,8000,200,4,4,,, +Llama-xLAM-2-8b-fc-r,8000,200,8,8,,, +Llama-xLAM-2-8b-fc-r,8000,1024,1,1,,, +Llama-xLAM-2-8b-fc-r,8000,1024,2,2,,, +Llama-xLAM-2-8b-fc-r,8000,1024,4,4,,, +Llama-xLAM-2-8b-fc-r,8000,1024,8,8,,, \ No newline at end of file diff --git a/benchmarking/benchmarking_bundles/model_configs_example_datakrypto.csv b/benchmarking/benchmarking_bundles/model_configs_example_datakrypto.csv deleted file mode 100644 index 41e9d396f..000000000 --- a/benchmarking/benchmarking_bundles/model_configs_example_datakrypto.csv +++ /dev/null @@ -1,20 +0,0 @@ -model_name,input_tokens,output_tokens,num_requests,concurrent_requests,qps,qps_distribution,multimodal_img_size -Llama-xLAM-2-8b-fc-r,3900,100,1,1,,, -Llama-xLAM-2-8b-fc-r,3900,100,3,3,,, -Llama-xLAM-2-8b-fc-r,3900,100,5,5,,, -Llama-xLAM-2-8b-fc-r,3900,100,8,8,,, -Llama-xLAM-2-8b-fc-r,3900,100,16,16,,, -Llama-xLAM-2-8b-fc-r,3900,100,32,32,,, -Llama-xLAM-2-8b-fc-r,3900,100,64,64,,, -Llama-xLAM-2-8b-fc-r,3900,100,128,128,,, -Llama-xLAM-2-8b-fc-r,8000,100,1,1,,, -Llama-xLAM-2-8b-fc-r,8000,100,3,3,,, -Llama-xLAM-2-8b-fc-r,8000,100,5,5,,, -Llama-xLAM-2-8b-fc-r,8000,100,8,8,,, -Llama-xLAM-2-8b-fc-r,8000,100,16,16,,, -Llama-xLAM-2-8b-fc-r,8000,100,32,32,,, -Llama-xLAM-2-8b-fc-r,8000,100,64,64,,, -Llama-xLAM-2-8b-fc-r,16000,100,1,1,,, -Llama-xLAM-2-8b-fc-r,16000,100,3,3,,, -Llama-xLAM-2-8b-fc-r,16000,100,5,5,,, -Llama-xLAM-2-8b-fc-r,16000,100,8,8,,, \ No newline at end of file diff --git a/benchmarking/benchmarking_bundles/model_configs_example_datakrypto_non_encrypted.csv b/benchmarking/benchmarking_bundles/model_configs_example_datakrypto_non_encrypted.csv new file mode 100644 index 000000000..dda5bc115 --- /dev/null +++ b/benchmarking/benchmarking_bundles/model_configs_example_datakrypto_non_encrypted.csv @@ -0,0 +1,33 @@ +model_name,input_tokens,output_tokens,num_requests,concurrent_requests,qps,qps_distribution,multimodal_img_size +Llama-xLAM-2-8b-fc-r,2000,100,1,1,,, +Llama-xLAM-2-8b-fc-r,2000,100,2,2,,, +Llama-xLAM-2-8b-fc-r,2000,100,4,4,,, +Llama-xLAM-2-8b-fc-r,2000,100,8,8,,, +Llama-xLAM-2-8b-fc-r,2000,100,16,16,,, +Llama-xLAM-2-8b-fc-r,2000,100,32,32,,, +Llama-xLAM-2-8b-fc-r,2000,1024,1,1,,, +Llama-xLAM-2-8b-fc-r,2000,1024,2,2,,, +Llama-xLAM-2-8b-fc-r,2000,1024,4,4,,, +Llama-xLAM-2-8b-fc-r,2000,1024,8,8,,, +Llama-xLAM-2-8b-fc-r,2000,1024,16,16,,, +Llama-xLAM-2-8b-fc-r,2000,1024,32,32,,, +Llama-xLAM-2-8b-fc-r,4000,100,1,1,,, +Llama-xLAM-2-8b-fc-r,4000,100,2,2,,, +Llama-xLAM-2-8b-fc-r,4000,100,4,4,,, +Llama-xLAM-2-8b-fc-r,4000,100,8,8,,, +Llama-xLAM-2-8b-fc-r,4000,100,16,16,,, +Llama-xLAM-2-8b-fc-r,4000,100,32,32,,, +Llama-xLAM-2-8b-fc-r,4000,1024,1,1,,, +Llama-xLAM-2-8b-fc-r,4000,1024,2,2,,, +Llama-xLAM-2-8b-fc-r,4000,1024,4,4,,, +Llama-xLAM-2-8b-fc-r,4000,1024,8,8,,, +Llama-xLAM-2-8b-fc-r,4000,1024,16,16,,, +Llama-xLAM-2-8b-fc-r,4000,1024,32,32,,, +Llama-xLAM-2-8b-fc-r,8000,200,1,1,,, +Llama-xLAM-2-8b-fc-r,8000,200,2,2,,, +Llama-xLAM-2-8b-fc-r,8000,200,4,4,,, +Llama-xLAM-2-8b-fc-r,8000,200,8,8,,, +Llama-xLAM-2-8b-fc-r,8000,1024,1,1,,, +Llama-xLAM-2-8b-fc-r,8000,1024,2,2,,, +Llama-xLAM-2-8b-fc-r,8000,1024,4,4,,, +Llama-xLAM-2-8b-fc-r,8000,1024,8,8,,, \ No newline at end of file From 47e6dede4ada8241873732e3c87262cd4ed8987b Mon Sep 17 00:00:00 2001 From: jhpiedrahitao Date: Mon, 23 Feb 2026 10:53:08 -0500 Subject: [PATCH 08/17] minor change --- benchmarking/src/llmperf/sambanova_client.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarking/src/llmperf/sambanova_client.py b/benchmarking/src/llmperf/sambanova_client.py index aa5bce88f..d8aebebe0 100644 --- a/benchmarking/src/llmperf/sambanova_client.py +++ b/benchmarking/src/llmperf/sambanova_client.py @@ -354,7 +354,7 @@ def compute_metrics(self, metrics: Dict[str, Any]) -> Tuple[Dict[str, Any], str] # print(f'url :{url}') # print(f'headers :{headers}') - print(f'json_data :{json.dumps(json_data)}') + # print(f'json_data :{json.dumps(json_data)}') with requests.post(url, headers=headers, json=json_data, stream=self.request_config.is_stream_mode) as response: # print(f'response: {response.content}') From 7a6b0a9372ab19d313f6b2667bc4fb8fd5e7aef9 Mon Sep 17 00:00:00 2001 From: jhpiedrahitao Date: Mon, 23 Feb 2026 11:00:44 -0500 Subject: [PATCH 09/17] add encrypted config --- benchmarking/benchmarking_bundles/config.yaml | 4 ++-- .../model_configs_example.csv | 22 +------------------ ...l_configs_example_datakrypto_encrypted.csv | 13 +++++++++++ 3 files changed, 16 insertions(+), 23 deletions(-) create mode 100644 benchmarking/benchmarking_bundles/model_configs_example_datakrypto_encrypted.csv diff --git a/benchmarking/benchmarking_bundles/config.yaml b/benchmarking/benchmarking_bundles/config.yaml index 6fa71b30c..4dd33bac3 100644 --- a/benchmarking/benchmarking_bundles/config.yaml +++ b/benchmarking/benchmarking_bundles/config.yaml @@ -1,7 +1,7 @@ model_configs_path: '../benchmarking/benchmarking_bundles/model_configs_example.csv' llm_api: 'sncloud' -output_files_dir: '../benchmarking/data/bundle_tests/datakrypto/speed_bench_test_non_encrypted' -consolidated_results_dir: '../benchmarking/data/bundle_tests/datakrypto/speed_bench_test_non_encrypted/consolidated_results' +output_files_dir: '../benchmarking/data/bundle_tests/datakrypto/speed_bench_test_encrypted' +consolidated_results_dir: '../benchmarking/data/bundle_tests/datakrypto/speed_bench_test_encrypted/consolidated_results' timeout: 3600 time_delay: 0 # Row-level concurrency diff --git a/benchmarking/benchmarking_bundles/model_configs_example.csv b/benchmarking/benchmarking_bundles/model_configs_example.csv index dda5bc115..6d510fbe5 100644 --- a/benchmarking/benchmarking_bundles/model_configs_example.csv +++ b/benchmarking/benchmarking_bundles/model_configs_example.csv @@ -10,24 +10,4 @@ Llama-xLAM-2-8b-fc-r,2000,1024,2,2,,, Llama-xLAM-2-8b-fc-r,2000,1024,4,4,,, Llama-xLAM-2-8b-fc-r,2000,1024,8,8,,, Llama-xLAM-2-8b-fc-r,2000,1024,16,16,,, -Llama-xLAM-2-8b-fc-r,2000,1024,32,32,,, -Llama-xLAM-2-8b-fc-r,4000,100,1,1,,, -Llama-xLAM-2-8b-fc-r,4000,100,2,2,,, -Llama-xLAM-2-8b-fc-r,4000,100,4,4,,, -Llama-xLAM-2-8b-fc-r,4000,100,8,8,,, -Llama-xLAM-2-8b-fc-r,4000,100,16,16,,, -Llama-xLAM-2-8b-fc-r,4000,100,32,32,,, -Llama-xLAM-2-8b-fc-r,4000,1024,1,1,,, -Llama-xLAM-2-8b-fc-r,4000,1024,2,2,,, -Llama-xLAM-2-8b-fc-r,4000,1024,4,4,,, -Llama-xLAM-2-8b-fc-r,4000,1024,8,8,,, -Llama-xLAM-2-8b-fc-r,4000,1024,16,16,,, -Llama-xLAM-2-8b-fc-r,4000,1024,32,32,,, -Llama-xLAM-2-8b-fc-r,8000,200,1,1,,, -Llama-xLAM-2-8b-fc-r,8000,200,2,2,,, -Llama-xLAM-2-8b-fc-r,8000,200,4,4,,, -Llama-xLAM-2-8b-fc-r,8000,200,8,8,,, -Llama-xLAM-2-8b-fc-r,8000,1024,1,1,,, -Llama-xLAM-2-8b-fc-r,8000,1024,2,2,,, -Llama-xLAM-2-8b-fc-r,8000,1024,4,4,,, -Llama-xLAM-2-8b-fc-r,8000,1024,8,8,,, \ No newline at end of file +Llama-xLAM-2-8b-fc-r,2000,1024,32,32,,, \ No newline at end of file diff --git a/benchmarking/benchmarking_bundles/model_configs_example_datakrypto_encrypted.csv b/benchmarking/benchmarking_bundles/model_configs_example_datakrypto_encrypted.csv new file mode 100644 index 000000000..6d510fbe5 --- /dev/null +++ b/benchmarking/benchmarking_bundles/model_configs_example_datakrypto_encrypted.csv @@ -0,0 +1,13 @@ +model_name,input_tokens,output_tokens,num_requests,concurrent_requests,qps,qps_distribution,multimodal_img_size +Llama-xLAM-2-8b-fc-r,2000,100,1,1,,, +Llama-xLAM-2-8b-fc-r,2000,100,2,2,,, +Llama-xLAM-2-8b-fc-r,2000,100,4,4,,, +Llama-xLAM-2-8b-fc-r,2000,100,8,8,,, +Llama-xLAM-2-8b-fc-r,2000,100,16,16,,, +Llama-xLAM-2-8b-fc-r,2000,100,32,32,,, +Llama-xLAM-2-8b-fc-r,2000,1024,1,1,,, +Llama-xLAM-2-8b-fc-r,2000,1024,2,2,,, +Llama-xLAM-2-8b-fc-r,2000,1024,4,4,,, +Llama-xLAM-2-8b-fc-r,2000,1024,8,8,,, +Llama-xLAM-2-8b-fc-r,2000,1024,16,16,,, +Llama-xLAM-2-8b-fc-r,2000,1024,32,32,,, \ No newline at end of file From d4761e6f51ad765ae65a09e8a58bf9e5a00734db Mon Sep 17 00:00:00 2001 From: jhpiedrahitao Date: Mon, 23 Feb 2026 11:04:52 -0500 Subject: [PATCH 10/17] fix typo --- .../model_configs_example.csv | 24 +++++++++---------- ...l_configs_example_datakrypto_encrypted.csv | 24 +++++++++---------- 2 files changed, 24 insertions(+), 24 deletions(-) diff --git a/benchmarking/benchmarking_bundles/model_configs_example.csv b/benchmarking/benchmarking_bundles/model_configs_example.csv index 6d510fbe5..0d30a59cd 100644 --- a/benchmarking/benchmarking_bundles/model_configs_example.csv +++ b/benchmarking/benchmarking_bundles/model_configs_example.csv @@ -1,13 +1,13 @@ model_name,input_tokens,output_tokens,num_requests,concurrent_requests,qps,qps_distribution,multimodal_img_size -Llama-xLAM-2-8b-fc-r,2000,100,1,1,,, -Llama-xLAM-2-8b-fc-r,2000,100,2,2,,, -Llama-xLAM-2-8b-fc-r,2000,100,4,4,,, -Llama-xLAM-2-8b-fc-r,2000,100,8,8,,, -Llama-xLAM-2-8b-fc-r,2000,100,16,16,,, -Llama-xLAM-2-8b-fc-r,2000,100,32,32,,, -Llama-xLAM-2-8b-fc-r,2000,1024,1,1,,, -Llama-xLAM-2-8b-fc-r,2000,1024,2,2,,, -Llama-xLAM-2-8b-fc-r,2000,1024,4,4,,, -Llama-xLAM-2-8b-fc-r,2000,1024,8,8,,, -Llama-xLAM-2-8b-fc-r,2000,1024,16,16,,, -Llama-xLAM-2-8b-fc-r,2000,1024,32,32,,, \ No newline at end of file +Llama-xLAM-2-8b-fc-r-encrypted,2000,100,1,1,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,100,2,2,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,100,4,4,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,100,8,8,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,100,16,16,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,100,32,32,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,1,1,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,2,2,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,4,4,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,8,8,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,16,16,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,32,32,,, \ No newline at end of file diff --git a/benchmarking/benchmarking_bundles/model_configs_example_datakrypto_encrypted.csv b/benchmarking/benchmarking_bundles/model_configs_example_datakrypto_encrypted.csv index 6d510fbe5..0d30a59cd 100644 --- a/benchmarking/benchmarking_bundles/model_configs_example_datakrypto_encrypted.csv +++ b/benchmarking/benchmarking_bundles/model_configs_example_datakrypto_encrypted.csv @@ -1,13 +1,13 @@ model_name,input_tokens,output_tokens,num_requests,concurrent_requests,qps,qps_distribution,multimodal_img_size -Llama-xLAM-2-8b-fc-r,2000,100,1,1,,, -Llama-xLAM-2-8b-fc-r,2000,100,2,2,,, -Llama-xLAM-2-8b-fc-r,2000,100,4,4,,, -Llama-xLAM-2-8b-fc-r,2000,100,8,8,,, -Llama-xLAM-2-8b-fc-r,2000,100,16,16,,, -Llama-xLAM-2-8b-fc-r,2000,100,32,32,,, -Llama-xLAM-2-8b-fc-r,2000,1024,1,1,,, -Llama-xLAM-2-8b-fc-r,2000,1024,2,2,,, -Llama-xLAM-2-8b-fc-r,2000,1024,4,4,,, -Llama-xLAM-2-8b-fc-r,2000,1024,8,8,,, -Llama-xLAM-2-8b-fc-r,2000,1024,16,16,,, -Llama-xLAM-2-8b-fc-r,2000,1024,32,32,,, \ No newline at end of file +Llama-xLAM-2-8b-fc-r-encrypted,2000,100,1,1,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,100,2,2,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,100,4,4,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,100,8,8,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,100,16,16,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,100,32,32,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,1,1,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,2,2,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,4,4,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,8,8,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,16,16,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,32,32,,, \ No newline at end of file From 9adccab382df381231201f8ad62d334ec2c648fa Mon Sep 17 00:00:00 2001 From: Rodrigo Maldonado Date: Mon, 23 Feb 2026 18:20:56 -0500 Subject: [PATCH 11/17] added datakrypto metrics to consolidated report --- .../synthetic_performance_eval_script.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/benchmarking/benchmarking_bundles/synthetic_performance_eval_script.py b/benchmarking/benchmarking_bundles/synthetic_performance_eval_script.py index b267319f2..adf4e0bd6 100644 --- a/benchmarking/benchmarking_bundles/synthetic_performance_eval_script.py +++ b/benchmarking/benchmarking_bundles/synthetic_performance_eval_script.py @@ -304,6 +304,18 @@ def get_batching_frequencies(uuid: str) -> Dict[int, int]: 'switching_time', 'request_batching_frequencies', 'representative_batch_size', + 'total_encryption_time_ms_min', + 'total_encryption_time_ms_mean', + 'total_encryption_time_ms_quantiles_p50', + 'total_encryption_time_ms_max', + 'total_decryption_time_ms_min', + 'total_decryption_time_ms_mean', + 'total_decryption_time_ms_quantiles_p50', + 'total_decryption_time_ms_max', + 'server_network_latency_ms_min', + 'server_network_latency_ms_mean', + 'server_network_latency_ms_quantiles_p50', + 'server_network_latency_ms_max' ] # Remove missing columns safely From 302ab970a2f9e353df248207b9cb1e290d9b4acb Mon Sep 17 00:00:00 2001 From: jhpiedrahitao Date: Wed, 25 Feb 2026 09:56:58 -0500 Subject: [PATCH 12/17] update configs --- benchmarking/benchmarking_bundles/config.yaml | 4 ++-- .../model_configs_example.csv | 24 +++++++++---------- ..._example_datakrypto_encrypted_multiple.csv | 13 ++++++++++ ...nfigs_example_datakrypto_non_encrypted.csv | 22 +---------------- ...mple_datakrypto_non_encrypted_multiple.csv | 13 ++++++++++ 5 files changed, 41 insertions(+), 35 deletions(-) create mode 100644 benchmarking/benchmarking_bundles/model_configs_example_datakrypto_encrypted_multiple.csv create mode 100644 benchmarking/benchmarking_bundles/model_configs_example_datakrypto_non_encrypted_multiple.csv diff --git a/benchmarking/benchmarking_bundles/config.yaml b/benchmarking/benchmarking_bundles/config.yaml index 4dd33bac3..13a2b5e7f 100644 --- a/benchmarking/benchmarking_bundles/config.yaml +++ b/benchmarking/benchmarking_bundles/config.yaml @@ -1,7 +1,7 @@ model_configs_path: '../benchmarking/benchmarking_bundles/model_configs_example.csv' llm_api: 'sncloud' -output_files_dir: '../benchmarking/data/bundle_tests/datakrypto/speed_bench_test_encrypted' -consolidated_results_dir: '../benchmarking/data/bundle_tests/datakrypto/speed_bench_test_encrypted/consolidated_results' +output_files_dir: '../benchmarking/data/bundle_tests/datakrypto/speed_bench_test_encrypted_multiple' +consolidated_results_dir: '../benchmarking/data/bundle_tests/datakrypto/speed_bench_test_encrypted_multiple/consolidated_results' timeout: 3600 time_delay: 0 # Row-level concurrency diff --git a/benchmarking/benchmarking_bundles/model_configs_example.csv b/benchmarking/benchmarking_bundles/model_configs_example.csv index 0d30a59cd..a7391adf0 100644 --- a/benchmarking/benchmarking_bundles/model_configs_example.csv +++ b/benchmarking/benchmarking_bundles/model_configs_example.csv @@ -1,13 +1,13 @@ model_name,input_tokens,output_tokens,num_requests,concurrent_requests,qps,qps_distribution,multimodal_img_size -Llama-xLAM-2-8b-fc-r-encrypted,2000,100,1,1,,, -Llama-xLAM-2-8b-fc-r-encrypted,2000,100,2,2,,, -Llama-xLAM-2-8b-fc-r-encrypted,2000,100,4,4,,, -Llama-xLAM-2-8b-fc-r-encrypted,2000,100,8,8,,, -Llama-xLAM-2-8b-fc-r-encrypted,2000,100,16,16,,, -Llama-xLAM-2-8b-fc-r-encrypted,2000,100,32,32,,, -Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,1,1,,, -Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,2,2,,, -Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,4,4,,, -Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,8,8,,, -Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,16,16,,, -Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,32,32,,, \ No newline at end of file +Llama-xLAM-2-8b-fc-r-encrypted,2000,100,10,1,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,100,20,2,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,100,40,4,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,100,80,8,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,100,160,16,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,100,320,32,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,10,1,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,20,2,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,40,4,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,80,8,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,160,16,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,320,32,,, \ No newline at end of file diff --git a/benchmarking/benchmarking_bundles/model_configs_example_datakrypto_encrypted_multiple.csv b/benchmarking/benchmarking_bundles/model_configs_example_datakrypto_encrypted_multiple.csv new file mode 100644 index 000000000..a7391adf0 --- /dev/null +++ b/benchmarking/benchmarking_bundles/model_configs_example_datakrypto_encrypted_multiple.csv @@ -0,0 +1,13 @@ +model_name,input_tokens,output_tokens,num_requests,concurrent_requests,qps,qps_distribution,multimodal_img_size +Llama-xLAM-2-8b-fc-r-encrypted,2000,100,10,1,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,100,20,2,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,100,40,4,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,100,80,8,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,100,160,16,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,100,320,32,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,10,1,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,20,2,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,40,4,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,80,8,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,160,16,,, +Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,320,32,,, \ No newline at end of file diff --git a/benchmarking/benchmarking_bundles/model_configs_example_datakrypto_non_encrypted.csv b/benchmarking/benchmarking_bundles/model_configs_example_datakrypto_non_encrypted.csv index dda5bc115..6d510fbe5 100644 --- a/benchmarking/benchmarking_bundles/model_configs_example_datakrypto_non_encrypted.csv +++ b/benchmarking/benchmarking_bundles/model_configs_example_datakrypto_non_encrypted.csv @@ -10,24 +10,4 @@ Llama-xLAM-2-8b-fc-r,2000,1024,2,2,,, Llama-xLAM-2-8b-fc-r,2000,1024,4,4,,, Llama-xLAM-2-8b-fc-r,2000,1024,8,8,,, Llama-xLAM-2-8b-fc-r,2000,1024,16,16,,, -Llama-xLAM-2-8b-fc-r,2000,1024,32,32,,, -Llama-xLAM-2-8b-fc-r,4000,100,1,1,,, -Llama-xLAM-2-8b-fc-r,4000,100,2,2,,, -Llama-xLAM-2-8b-fc-r,4000,100,4,4,,, -Llama-xLAM-2-8b-fc-r,4000,100,8,8,,, -Llama-xLAM-2-8b-fc-r,4000,100,16,16,,, -Llama-xLAM-2-8b-fc-r,4000,100,32,32,,, -Llama-xLAM-2-8b-fc-r,4000,1024,1,1,,, -Llama-xLAM-2-8b-fc-r,4000,1024,2,2,,, -Llama-xLAM-2-8b-fc-r,4000,1024,4,4,,, -Llama-xLAM-2-8b-fc-r,4000,1024,8,8,,, -Llama-xLAM-2-8b-fc-r,4000,1024,16,16,,, -Llama-xLAM-2-8b-fc-r,4000,1024,32,32,,, -Llama-xLAM-2-8b-fc-r,8000,200,1,1,,, -Llama-xLAM-2-8b-fc-r,8000,200,2,2,,, -Llama-xLAM-2-8b-fc-r,8000,200,4,4,,, -Llama-xLAM-2-8b-fc-r,8000,200,8,8,,, -Llama-xLAM-2-8b-fc-r,8000,1024,1,1,,, -Llama-xLAM-2-8b-fc-r,8000,1024,2,2,,, -Llama-xLAM-2-8b-fc-r,8000,1024,4,4,,, -Llama-xLAM-2-8b-fc-r,8000,1024,8,8,,, \ No newline at end of file +Llama-xLAM-2-8b-fc-r,2000,1024,32,32,,, \ No newline at end of file diff --git a/benchmarking/benchmarking_bundles/model_configs_example_datakrypto_non_encrypted_multiple.csv b/benchmarking/benchmarking_bundles/model_configs_example_datakrypto_non_encrypted_multiple.csv new file mode 100644 index 000000000..2ed06ebce --- /dev/null +++ b/benchmarking/benchmarking_bundles/model_configs_example_datakrypto_non_encrypted_multiple.csv @@ -0,0 +1,13 @@ +model_name,input_tokens,output_tokens,num_requests,concurrent_requests,qps,qps_distribution,multimodal_img_size +Llama-xLAM-2-8b-fc-r,2000,100,10,1,,, +Llama-xLAM-2-8b-fc-r,2000,100,20,2,,, +Llama-xLAM-2-8b-fc-r,2000,100,40,4,,, +Llama-xLAM-2-8b-fc-r,2000,100,80,8,,, +Llama-xLAM-2-8b-fc-r,2000,100,160,16,,, +Llama-xLAM-2-8b-fc-r,2000,100,320,32,,, +Llama-xLAM-2-8b-fc-r,2000,1024,10,1,,, +Llama-xLAM-2-8b-fc-r,2000,1024,20,2,,, +Llama-xLAM-2-8b-fc-r,2000,1024,40,4,,, +Llama-xLAM-2-8b-fc-r,2000,1024,80,8,,, +Llama-xLAM-2-8b-fc-r,2000,1024,160,16,,, +Llama-xLAM-2-8b-fc-r,2000,1024,320,32,,, \ No newline at end of file From f41abc7d8d1c367b98c1965db87f708dfc8522a1 Mon Sep 17 00:00:00 2001 From: jhpiedrahitao Date: Fri, 27 Feb 2026 19:43:41 -0500 Subject: [PATCH 13/17] update bundle reports --- .../datakrypto_tee_performance_report.pdf | Bin 0 -> 693962 bytes .../datakrypto_tee_report.py | 979 + .../datakrypto-tee-performance-analysis.ipynb | 27218 ++++++++++++++++ 3 files changed, 28197 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z{udo?GWh2|jAo5KbFM20bk*l+iVCZ&=hAE_P-k$|o@o$!{r^6~{73$u|1ae#3ZB6m z*iPqv@vhE^|BL_P$(76hJYS>W^Zn=FxL3qK{sE()Ve(J(euCou3j`A0rKT*&|UzX5o(=NA9~ literal 0 HcmV?d00001 diff --git a/benchmarking/benchmarking_bundles/datakrypto_tee_report.py b/benchmarking/benchmarking_bundles/datakrypto_tee_report.py new file mode 100644 index 000000000..30e7beb2b --- /dev/null +++ b/benchmarking/benchmarking_bundles/datakrypto_tee_report.py @@ -0,0 +1,979 @@ +""" +DataKrypto TEE Performance Analysis — PDF Report Generator +Run from repo root with virtual env active: + python benchmarking/benchmarking_bundles/datakrypto_tee_report.py +""" + +import io +import os +import struct +import sys +import warnings + +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt +import matplotlib.patches as mpatches +import numpy as np +import pandas as pd + +warnings.filterwarnings('ignore') + +# ── reportlab ──────────────────────────────────────────────────────────────── +from reportlab.lib import colors +from reportlab.lib.enums import TA_CENTER, TA_JUSTIFY, TA_LEFT +from reportlab.lib.pagesizes import A4 +from reportlab.lib.styles import ParagraphStyle, getSampleStyleSheet +from reportlab.lib.units import cm, inch +from reportlab.platypus import ( + HRFlowable, Image, PageBreak, Paragraph, Spacer, Table, TableStyle, + SimpleDocTemplate, KeepTogether, +) + +# ── Paths ──────────────────────────────────────────────────────────────────── +SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__)) +DATA_ROOT = os.path.abspath(os.path.join(SCRIPT_DIR, '../data/datakrypto')) +OUT_PDF = os.path.join(SCRIPT_DIR, 'datakrypto_tee_performance_report.pdf') + +for p in [SCRIPT_DIR, os.path.join(SCRIPT_DIR, '../../'), + os.path.join(SCRIPT_DIR, '../../../')]: + if p not in sys.path: + sys.path.insert(0, p) + +# ── Palette ────────────────────────────────────────────────────────────────── +C_ENC = '#EF553B' +C_PLAIN = '#636EFA' +C_NET = '#FF7F0E' +C_ENCR = '#D62728' +C_DEC = '#2CA02C' +C_OTHER = '#9467BD' +C_NAVY = '#0d1b2a' +C_BLUE2 = '#1b4f72' +C_LIGHT = '#eaf4fb' +C_FIND = '#fef9e7' +C_WARN = '#fdedec' + +CONC_ORDER = [1, 2, 4, 8, 16, 32] + +# ═══════════════════════════════════════════════════════════════════════════ +# DATA LOADING +# ═══════════════════════════════════════════════════════════════════════════ + +DATASETS = { + 'enc_simple': { + 'consolidated': f'{DATA_ROOT}/speed_bench_test_encrypted_simple/consolidated_results/20260225-155057.028751.xlsx', + 'encrypted': True, 'run_type': 'Single Run (1x)', 'label': 'Encrypted', + }, + 'plain_simple': { + 'consolidated': f'{DATA_ROOT}/speed_bench_test_non_encrypted_simple/consolidated_results/20260227-204607.930274.xlsx', + 'encrypted': False, 'run_type': 'Single Run (1x)', 'label': 'Non-Encrypted', + }, + 'enc_multiple': { + 'consolidated': f'{DATA_ROOT}/speed_bench_test_encrypted_multiple/consolidated_results/20260225-150147.253373.xlsx', + 'encrypted': True, 'run_type': 'Multi Run (10x)', 'label': 'Encrypted', + }, + 'plain_multiple': { + 'consolidated': f'{DATA_ROOT}/speed_bench_test_non_encrypted_multiple/consolidated_results/20260227-205436.463636.xlsx', + 'encrypted': False, 'run_type': 'Multi Run (10x)', 'label': 'Non-Encrypted', + }, +} + +dfs = [] +for key, meta in DATASETS.items(): + df = pd.read_excel(meta['consolidated']) + df['dataset_key'] = key + df['encrypted'] = meta['encrypted'] + df['run_type'] = meta['run_type'] + df['label'] = meta['label'] + dfs.append(df) + +df_all = pd.concat(dfs, ignore_index=True) +df_all['total_requests'] = df_all['num_completed_requests'] + df_all['number_errors'] +df_all['error_rate_pct'] = ( + df_all['number_errors'] / df_all['total_requests'].replace(0, np.nan) +) * 100 + +DIMS = ['run_type', 'num_output_tokens', 'num_concurrent_requests'] + +df_multi = df_all[df_all['run_type'] == 'Multi Run (10x)'] +df_enc_m = df_multi[df_multi['label'] == 'Encrypted'] +df_plain_m = df_multi[df_multi['label'] == 'Non-Encrypted'] + +# TEE overhead attribution (multi run, merged) +df_attr = df_enc_m.merge( + df_plain_m[DIMS + ['client_ttft_s_p50']].rename( + columns={'client_ttft_s_p50': 'client_ttft_plain'} + ), + on=DIMS, +) +df_attr['ttft_delta'] = df_attr['client_ttft_s_p50'] - df_attr['client_ttft_plain'] +df_attr['network_s'] = df_attr['server_network_latency_ms_mean'] / 1000 +df_attr['enc_s'] = df_attr['total_encryption_time_ms_mean'] / 1000 +df_attr['dec_s'] = df_attr['total_decryption_time_ms_mean'] / 1000 +df_attr['residual_s'] = (df_attr['ttft_delta'] - df_attr['network_s'] + - df_attr['enc_s'] - df_attr['dec_s']).clip(lower=0) + +# ═══════════════════════════════════════════════════════════════════════════ +# CHART HELPERS +# ═══════════════════════════════════════════════════════════════════════════ + +PLT_STYLE = { + 'axes.spines.top': False, + 'axes.spines.right': False, + 'axes.grid': True, + 'grid.alpha': 0.35, + 'axes.labelsize': 9, + 'xtick.labelsize': 8, + 'ytick.labelsize': 8, + 'legend.fontsize': 8, + 'figure.facecolor': 'white', + 'axes.facecolor': 'white', +} + +def fig_to_image(fig, width_cm=16): + buf = io.BytesIO() + fig.savefig(buf, format='png', dpi=160, bbox_inches='tight') + plt.close(fig) + # Read PNG dimensions from header (bytes 16-23 after PNG signature + IHDR tag) + buf.seek(16) + px_w = struct.unpack('>I', buf.read(4))[0] + px_h = struct.unpack('>I', buf.read(4))[0] + buf.seek(0) + width_pts = width_cm * cm + height_pts = width_pts * px_h / px_w + img = Image(buf, width=width_pts, height=height_pts) + img.hAlign = 'CENTER' + return img + + +def line_chart(ax, x, y_enc, y_plain, ylabel, title, log_x=True): + """Shared enc vs plain line chart helper.""" + kw = dict(marker='o', markersize=4, linewidth=1.8) + ax.plot(x, y_enc, color=C_ENC, label='Encrypted', **kw) + ax.plot(x, y_plain, color=C_PLAIN, label='Non-Encrypted', **kw, linestyle='--') + if log_x: + ax.set_xscale('log', base=2) + ax.set_xticks(CONC_ORDER) + ax.set_xticklabels(CONC_ORDER) + ax.set_xlabel('Concurrency') + ax.set_ylabel(ylabel) + ax.set_title(title, fontsize=9, fontweight='bold') + ax.legend(framealpha=0.6) + + +# ═══════════════════════════════════════════════════════════════════════════ +# CHART 1 — SERVER METRICS (assumption validation) +# ═══════════════════════════════════════════════════════════════════════════ + +def make_chart_server(): + with plt.rc_context(PLT_STYLE): + fig, axes = plt.subplots(2, 2, figsize=(11, 7)) + fig.suptitle('Server-Side Metrics — Encrypted vs Non-Encrypted', + fontsize=11, fontweight='bold', y=1.01) + + for col, out_tok in enumerate([100, 1024]): + enc_sub = df_enc_m[df_enc_m['num_output_tokens'] == out_tok].sort_values('num_concurrent_requests') + plain_sub = df_plain_m[df_plain_m['num_output_tokens'] == out_tok].sort_values('num_concurrent_requests') + x = enc_sub['num_concurrent_requests'].values + + line_chart( + axes[0, col], x, + enc_sub['server_ttft_s_p50'].values, + plain_sub['server_ttft_s_p50'].values, + 'Server TTFT p50 (s)', + f'Server TTFT — {out_tok} output tokens', + ) + line_chart( + axes[1, col], x, + enc_sub['server_output_token_per_s_p50'].values, + plain_sub['server_output_token_per_s_p50'].values, + 'Output Tok/s p50 (per request)', + f'Server Output Throughput — {out_tok} output tokens', + ) + + fig.tight_layout() + return fig_to_image(fig) + + +# ═══════════════════════════════════════════════════════════════════════════ +# CHART 2 — TEE OVERHEAD BREAKDOWN +# ═══════════════════════════════════════════════════════════════════════════ + +def make_chart_tee_breakdown(): + with plt.rc_context(PLT_STYLE): + fig, axes = plt.subplots(1, 2, figsize=(11, 4.5)) + fig.suptitle('TEE Overhead Breakdown per Request (Encrypted, Multi Run)', + fontsize=11, fontweight='bold') + + for col, out_tok in enumerate([100, 1024]): + sub = df_attr[df_attr['num_output_tokens'] == out_tok].sort_values('num_concurrent_requests') + x = np.arange(len(sub)) + xl = [str(c) for c in sub['num_concurrent_requests'].values] + ax = axes[col] + + b1 = ax.bar(x, sub['network_s'], color=C_NET, label='Network Hop', alpha=0.9) + b2 = ax.bar(x, sub['enc_s'], color=C_ENCR, label='Input Encryption', alpha=0.9, + bottom=sub['network_s']) + b3 = ax.bar(x, sub['dec_s'], color=C_DEC, label='Output Decryption',alpha=0.9, + bottom=sub['network_s'] + sub['enc_s']) + b4 = ax.bar(x, sub['residual_s'], color=C_OTHER,label='Residual', alpha=0.7, + bottom=sub['network_s'] + sub['enc_s'] + sub['dec_s']) + + # Annotate network % on each bar + for i, row in sub.iterrows(): + total = row['ttft_delta'] + if total > 0: + pct = row['network_s'] / row['client_ttft_s_p50'] * 100 + ax.text(list(sub.index).index(i), + row['network_s'] / 2, + f'{pct:.0f}%', ha='center', va='center', + fontsize=6.5, color='white', fontweight='bold') + + ax.set_xticks(x) + ax.set_xticklabels(xl) + ax.set_xlabel('Concurrency') + ax.set_ylabel('Overhead (s)') + ax.set_title(f'TEE Overhead — {out_tok} output tokens', fontsize=9, fontweight='bold') + if col == 0: + ax.legend(loc='upper left', fontsize=7.5, framealpha=0.7) + + fig.tight_layout() + return fig_to_image(fig) + + +# ═══════════════════════════════════════════════════════════════════════════ +# CHART 3 — NETWORK LATENCY DETAIL +# ═══════════════════════════════════════════════════════════════════════════ + +def make_chart_network(): + df_enc_all = df_all[df_all['label'] == 'Encrypted'] + df_enc_s_all = df_enc_all[df_enc_all['run_type'] == 'Single Run (1x)'] + df_enc_m_all = df_enc_all[df_enc_all['run_type'] == 'Multi Run (10x)'] + + with plt.rc_context(PLT_STYLE): + fig, axes = plt.subplots(1, 2, figsize=(11, 4.5)) + fig.suptitle('TEE Network Latency vs Concurrency (Encrypted Model)', + fontsize=11, fontweight='bold') + + tok_colors = {100: '#1f77b4', 1024: '#ff7f0e'} + + for col, out_tok in enumerate([100, 1024]): + ax = axes[col] + for run_type, df_r, ls in [ + ('Single Run (1x)', df_enc_s_all, 'solid'), + ('Multi Run (10x)', df_enc_m_all, 'dashed'), + ]: + sub = df_r[df_r['num_output_tokens'] == out_tok].sort_values('num_concurrent_requests') + ax.plot( + sub['num_concurrent_requests'], + sub['server_network_latency_ms_mean'], + color=tok_colors[out_tok], + linestyle=ls, + marker='o', markersize=4, linewidth=1.8, + label=run_type, + ) + + ax.set_xscale('log', base=2) + ax.set_xticks(CONC_ORDER) + ax.set_xticklabels(CONC_ORDER) + ax.set_xlabel('Concurrency (log scale)') + ax.set_ylabel('Network Latency — mean (ms)') + ax.set_title(f'Network Latency — {out_tok} output tokens', fontsize=9, fontweight='bold') + ax.legend(framealpha=0.6) + ax.axvline(x=8, color='red', linestyle=':', linewidth=1.2, alpha=0.6) + ax.text(8.5, ax.get_ylim()[1] * 0.92, 'TEE core\nlimit (8)', + color='red', fontsize=6.5, alpha=0.8) + + fig.tight_layout() + return fig_to_image(fig) + + +# ═══════════════════════════════════════════════════════════════════════════ +# CHART 4 — CLIENT METRICS IMPACT +# ═══════════════════════════════════════════════════════════════════════════ + +def make_chart_client(): + with plt.rc_context(PLT_STYLE): + fig, axes = plt.subplots(2, 2, figsize=(11, 7)) + fig.suptitle('Client-Side Metrics — Encrypted vs Non-Encrypted (Multi Run)', + fontsize=11, fontweight='bold', y=1.01) + + for col, out_tok in enumerate([100, 1024]): + enc_sub = df_enc_m[df_enc_m['num_output_tokens'] == out_tok].sort_values('num_concurrent_requests') + plain_sub = df_plain_m[df_plain_m['num_output_tokens'] == out_tok].sort_values('num_concurrent_requests') + x = enc_sub['num_concurrent_requests'].values + + line_chart( + axes[0, col], x, + enc_sub['client_ttft_s_p50'].values, + plain_sub['client_ttft_s_p50'].values, + 'Client TTFT p50 (s)', + f'Client TTFT — {out_tok} output tokens', + ) + # Shade the TEE overhead gap + axes[0, col].fill_between( + x, + plain_sub['client_ttft_s_p50'].values, + enc_sub['client_ttft_s_p50'].values, + alpha=0.12, color=C_ENC, label='TEE overhead gap', + ) + + line_chart( + axes[1, col], x, + enc_sub['num_completed_requests_per_min'].values, + plain_sub['num_completed_requests_per_min'].values, + 'Completed Requests / min', + f'System Throughput — {out_tok} output tokens', + ) + + fig.tight_layout() + return fig_to_image(fig) + + +# ═══════════════════════════════════════════════════════════════════════════ +# CHART 5 — ENCRYPTION / DECRYPTION TIMES +# ═══════════════════════════════════════════════════════════════════════════ + +def make_chart_crypto_times(): + df_enc_all = df_all[df_all['label'] == 'Encrypted'] + tok_colors = {100: '#1f77b4', 1024: '#ff7f0e'} + run_styles = {'Single Run (1x)': 'solid', 'Multi Run (10x)': 'dashed'} + + with plt.rc_context(PLT_STYLE): + fig, axes = plt.subplots(1, 2, figsize=(11, 4.5)) + fig.suptitle('TEE Encryption & Decryption Times vs Concurrency', + fontsize=11, fontweight='bold') + + shown = set() + for run_type, ls in run_styles.items(): + for out_tok, color in tok_colors.items(): + sub = df_enc_all[(df_enc_all['run_type'] == run_type) & + (df_enc_all['num_output_tokens'] == out_tok) + ].sort_values('num_concurrent_requests') + name = f'{out_tok} out | {run_type}' + show = name not in shown + + axes[0].plot(sub['num_concurrent_requests'], + sub['total_encryption_time_ms_mean'], + color=color, linestyle=ls, marker='o', + markersize=4, linewidth=1.8, + label=name if show else None) + axes[1].plot(sub['num_concurrent_requests'], + sub['total_decryption_time_ms_mean'], + color=color, linestyle=ls, marker='o', + markersize=4, linewidth=1.8, + label=name if show else None) + shown.add(name) + + for ax, title, ylabel in [ + (axes[0], 'Input Encryption Time', 'Time (ms)'), + (axes[1], 'Output Decryption Time', 'Time (ms)'), + ]: + ax.set_xscale('log', base=2) + ax.set_xticks(CONC_ORDER) + ax.set_xticklabels(CONC_ORDER) + ax.set_xlabel('Concurrency (log scale)') + ax.set_ylabel(ylabel) + ax.set_title(title, fontsize=9, fontweight='bold') + ax.legend(fontsize=7, framealpha=0.6) + + fig.tight_layout() + return fig_to_image(fig) + + +# ═══════════════════════════════════════════════════════════════════════════ +# REPORTLAB STYLES +# ═══════════════════════════════════════════════════════════════════════════ + +def build_styles(): + base = getSampleStyleSheet() + + styles = { + 'cover_title': ParagraphStyle( + 'cover_title', fontSize=26, fontName='Helvetica-Bold', + textColor=colors.white, alignment=TA_CENTER, spaceAfter=8, + ), + 'cover_sub': ParagraphStyle( + 'cover_sub', fontSize=13, fontName='Helvetica', + textColor=colors.HexColor('#aed6f1'), alignment=TA_CENTER, spaceAfter=4, + ), + 'cover_date': ParagraphStyle( + 'cover_date', fontSize=10, fontName='Helvetica', + textColor=colors.HexColor('#85c1e9'), alignment=TA_CENTER, + ), + 'h1': ParagraphStyle( + 'h1', fontSize=14, fontName='Helvetica-Bold', + textColor=colors.HexColor(C_NAVY), spaceBefore=14, spaceAfter=6, + borderPad=4, + ), + 'h2': ParagraphStyle( + 'h2', fontSize=11, fontName='Helvetica-Bold', + textColor=colors.HexColor(C_BLUE2), spaceBefore=10, spaceAfter=4, + ), + 'body': ParagraphStyle( + 'body', fontSize=9.5, fontName='Helvetica', + textColor=colors.HexColor('#2c3e50'), leading=14, + alignment=TA_JUSTIFY, spaceAfter=6, + ), + 'bullet': ParagraphStyle( + 'bullet', fontSize=9.5, fontName='Helvetica', + textColor=colors.HexColor('#2c3e50'), leading=14, + leftIndent=14, spaceAfter=3, + ), + 'finding_title': ParagraphStyle( + 'finding_title', fontSize=10, fontName='Helvetica-Bold', + textColor=colors.HexColor('#1a5276'), spaceAfter=3, + ), + 'finding_body': ParagraphStyle( + 'finding_body', fontSize=9, fontName='Helvetica', + textColor=colors.HexColor('#2c3e50'), leading=13, + ), + 'caption': ParagraphStyle( + 'caption', fontSize=8, fontName='Helvetica-Oblique', + textColor=colors.HexColor('#7f8c8d'), alignment=TA_CENTER, + spaceBefore=2, spaceAfter=10, + ), + 'table_hdr': ParagraphStyle( + 'table_hdr', fontSize=8.5, fontName='Helvetica-Bold', + textColor=colors.white, alignment=TA_CENTER, + ), + 'table_cell': ParagraphStyle( + 'table_cell', fontSize=8.5, fontName='Helvetica', + textColor=colors.HexColor('#2c3e50'), alignment=TA_CENTER, + ), + } + return styles + + +def finding_box(title, body_paragraphs, styles, bg=C_FIND, border=C_BLUE2): + """Highlighted finding box.""" + content = [Paragraph(f'🔍 {title}', styles['finding_title'])] + for p in body_paragraphs: + content.append(Paragraph(p, styles['finding_body'])) + tbl = Table([[content]], colWidths=[16.5 * cm]) + tbl.setStyle(TableStyle([ + ('BACKGROUND', (0, 0), (-1, -1), colors.HexColor(bg)), + ('ROUNDEDCORNERS', [6]), + ('BOX', (0, 0), (-1, -1), 1.2, colors.HexColor(border)), + ('TOPPADDING', (0, 0), (-1, -1), 8), + ('BOTTOMPADDING',(0,0), (-1, -1), 8), + ('LEFTPADDING', (0, 0), (-1, -1), 10), + ('RIGHTPADDING',(0, 0), (-1, -1), 10), + ])) + return tbl + + +def warning_box(title, body, styles): + return finding_box(title, body, styles, bg=C_WARN, border='#e74c3c') + + +def section_rule(color=C_NAVY): + return HRFlowable(width='100%', thickness=1.5, + color=colors.HexColor(color), spaceAfter=6, spaceBefore=2) + + +# ═══════════════════════════════════════════════════════════════════════════ +# SUMMARY DATA TABLE +# ═══════════════════════════════════════════════════════════════════════════ + +def build_summary_table(styles): + """Side-by-side key metric table for multi run data.""" + + # Compute key numbers + rows_data = [] + for out_tok in [100, 1024]: + for conc in [1, 4, 8, 16, 32]: + enc_row = df_enc_m[(df_enc_m['num_output_tokens'] == out_tok) & + (df_enc_m['num_concurrent_requests'] == conc)] + plain_row = df_plain_m[(df_plain_m['num_output_tokens'] == out_tok) & + (df_plain_m['num_concurrent_requests'] == conc)] + if enc_row.empty or plain_row.empty: + continue + + e = enc_row.iloc[0] + p = plain_row.iloc[0] + + net_ms = e['server_network_latency_ms_mean'] + enc_ms = e['total_encryption_time_ms_mean'] + dec_ms = e['total_decryption_time_ms_mean'] + tee_total = net_ms + enc_ms + dec_ms + ttft_enc = e['client_ttft_s_p50'] + ttft_pln = p['client_ttft_s_p50'] + tee_pct = tee_total / 1000 / ttft_enc * 100 if ttft_enc else 0 + err_pct = e['error_rate_pct'] if pd.notna(e['error_rate_pct']) else 0 + + rows_data.append([ + f'{out_tok}', + f'{conc}', + f'{p["server_ttft_s_p50"]:.3f}', + f'{e["server_ttft_s_p50"]:.3f}', + f'{ttft_pln:.3f}', + f'{ttft_enc:.3f}', + f'{net_ms:.0f}', + f'{enc_ms:.1f}', + f'{dec_ms:.2f}', + f'{tee_pct:.0f}%', + f'{err_pct:.0f}%', + ]) + + headers = [ + 'Out\nTokens', 'Conc', + 'Server\nTTFT Plain', 'Server\nTTFT Enc', + 'Client\nTTFT Plain', 'Client\nTTFT Enc', + 'Network\n(ms)', 'Encrypt\n(ms)', 'Decrypt\n(ms)', + 'TEE %\nof TTFT', 'Error\nRate', + ] + + col_widths = [1.3*cm, 1.0*cm, 2.0*cm, 2.0*cm, 2.0*cm, 2.0*cm, + 1.7*cm, 1.6*cm, 1.6*cm, 1.6*cm, 1.5*cm] + + table_data = [[Paragraph(h, styles['table_hdr']) for h in headers]] + for rd in rows_data: + table_data.append([Paragraph(v, styles['table_cell']) for v in rd]) + + tbl = Table(table_data, colWidths=col_widths, repeatRows=1) + style = [ + ('BACKGROUND', (0, 0), (-1, 0), colors.HexColor(C_NAVY)), + ('TEXTCOLOR', (0, 0), (-1, 0), colors.white), + ('ALIGN', (0, 0), (-1, -1), 'CENTER'), + ('VALIGN', (0, 0), (-1, -1), 'MIDDLE'), + ('FONTSIZE', (0, 0), (-1, -1), 8), + ('ROWBACKGROUNDS',(0, 1), (-1, -1), [colors.white, colors.HexColor('#f2f3f4')]), + ('GRID', (0, 0), (-1, -1), 0.4, colors.HexColor('#bdc3c7')), + ('TOPPADDING', (0, 0), (-1, -1), 4), + ('BOTTOMPADDING',(0, 0), (-1, -1), 4), + ('ROWBACKGROUNDS',(0, 1), (-1, -1), [colors.white, colors.HexColor('#ebf5fb')]), + ] + # Highlight separator between 100 and 1024 token groups + n_100 = sum(1 for r in rows_data if r[0] == '100') + style.append(('LINEABOVE', (0, n_100 + 1), (-1, n_100 + 1), 1.5, colors.HexColor(C_NAVY))) + tbl.setStyle(TableStyle(style)) + return tbl + + +# ═══════════════════════════════════════════════════════════════════════════ +# PDF ASSEMBLY +# ═══════════════════════════════════════════════════════════════════════════ + +def build_pdf(): + from datetime import date as dt_date + + doc = SimpleDocTemplate( + OUT_PDF, + pagesize=A4, + leftMargin=1.8 * cm, + rightMargin=1.8 * cm, + topMargin=1.8 * cm, + bottomMargin=1.8 * cm, + ) + + S = build_styles() + story = [] + + # ── COVER ──────────────────────────────────────────────────────────── + # Dark header block + cover_bg = Table( + [[Paragraph('DataKrypto TEE', S['cover_title']),], + [Paragraph('Performance Impact Analysis', S['cover_title'])], + [Spacer(1, 0.3 * cm)], + [Paragraph('Encrypted vs Non-Encrypted Model Benchmarking Report', S['cover_sub'])], + [Spacer(1, 0.2 * cm)], + [Paragraph(f'Generated: {dt_date.today().strftime("%B %d, %Y")}', S['cover_date'])], + ], + colWidths=[16.5 * cm], + ) + cover_bg.setStyle(TableStyle([ + ('BACKGROUND', (0, 0), (-1, -1), colors.HexColor(C_NAVY)), + ('TOPPADDING', (0, 0), (-1, -1), 18), + ('BOTTOMPADDING',(0, 0), (-1, -1), 18), + ('LEFTPADDING', (0, 0), (-1, -1), 20), + ('RIGHTPADDING', (0, 0), (-1, -1), 20), + ('ROUNDEDCORNERS', [8]), + ])) + story.append(cover_bg) + story.append(Spacer(1, 0.5 * cm)) + + story.append(section_rule()) + story.append(Paragraph('Executive Summary', S['h1'])) + story.append(Paragraph( + 'This report benchmarks SambaNova inference performance when accessed directly ' + '(plain model) versus through the DataKrypto Trusted Execution Environment (TEE) ' + '(encrypted model). The TEE adds a confidential computing layer that encrypts the ' + 'user prompt before sending it to the inference server and decrypts each streaming ' + 'output token chunk before returning it to the client, introducing an extra network ' + 'hop in the request path.', + S['body'], + )) + story.append(Paragraph( + 'Tests were run across concurrencies of 1, 2, 4, 8, 16, and 32 simultaneous ' + 'HTTP requests, with output token targets of 100 and 1,024, using a fixed ' + '2,000 input-token prompt. Two test modes were evaluated: a Single Run (one round ' + 'of N simultaneous requests) and a Multi Run (10 sequential rounds of N simultaneous ' + 'requests) for statistical stability.', + S['body'], + )) + + story.append(Paragraph('Key Findings', S['h2'])) + findings = [ + ('✅ Server assumption confirmed: With endpoints on equivalent configurations, ' + 'server TTFT and tok/s are nearly identical for both models at concurrency 1–4 ' + '(~0.091 s TTFT, ~585–620 tok/s). The TEE has no impact on inference speed ' + 'inside the SambaNova engine.'), + ('🔀 TEE de-batching effect — a hidden server benefit: Because the TEE processes ' + 'requests sequentially per core, concurrent requests arrive at the server staggered, ' + 'keeping the effective batch small. At concurrency 8–32, encrypted server TTFT stays ' + 'flat at ~0.163 s while non-encrypted TTFT doubles to ~0.311 s. At concurrency ≥ 16 ' + 'for short responses (100 tokens), this server-side advantage outweighs the TEE network ' + 'overhead — encrypted client TTFT actually becomes lower than non-encrypted.'), + ('🌐 Network hop is the dominant overhead at low concurrency: The extra ' + 'Client → TEE → Server roundtrip accounts for the bulk of the client-observable latency ' + 'increase at concurrency 1–8. At concurrency 1 with 100 output tokens, TEE network ' + 'latency is ~425 ms (Multi Run); for 1,024 output tokens it rises to ~1,600 ms.'), + ('🔐 Crypto overhead is negligible: Input encryption costs ~5–10 ms per ' + 'request (near-constant, independent of output length). Cumulative output decryption ' + 'costs ~0.1–4 ms total across all streaming chunks — both are negligible compared ' + 'to the network hop.'), + ('⚠️ TEE queue saturation at concurrency > 8: The TEE has 8 cores processing ' + 'encryption/decryption sequentially. Beyond 8 simultaneous requests, requests queue ' + 'at the TEE, causing network latency to grow non-linearly and error rates to climb ' + '(up to ~19% at concurrency 32 for 100 output tokens).'), + ('📉 System throughput gap at scale: Non-Encrypted throughput scales near-linearly ' + 'with concurrency (153 → 848 req/min for 100 output tokens). Encrypted throughput peaks ' + 'at concurrency 2 (~180 req/min) and drops to ~28 req/min at concurrency 32 — ' + 'a ~30× gap driven by TEE queueing and errors above concurrency 8.'), + ('🔁 Decryption grows linearly with output tokens because each streaming ' + 'chunk is decrypted individually by the TEE. Each chunk decryption costs on the ' + 'order of microseconds; the growth is real but the absolute values remain tiny.'), + ] + for f in findings: + story.append(Paragraph(f'• {f}', S['bullet'])) + + story.append(PageBreak()) + + # ── SECTION 1: ARCHITECTURE ───────────────────────────────────────── + story.append(section_rule()) + story.append(Paragraph('1. Architecture & Test Setup', S['h1'])) + + story.append(Paragraph('Request Paths', S['h2'])) + arch_text = ( + 'Non-Encrypted: Client ──────────────────────────► SambaNova Inference Server
' + '                 (1 network hop — direct)

' + 'Encrypted:    Client ──► DataKrypto TEE ──► SambaNova Inference Server
' + '                         TEE encrypts input & decrypts each output chunk (streaming)
' + '                 (2 network hops total)' + ) + arch_box = Table([[Paragraph(arch_text, S['body'])]], colWidths=[16.5*cm]) + arch_box.setStyle(TableStyle([ + ('BACKGROUND', (0, 0), (-1, -1), colors.HexColor('#f8f9fa')), + ('BOX', (0, 0), (-1, -1), 1, colors.HexColor('#bdc3c7')), + ('TOPPADDING', (0, 0), (-1, -1), 10), + ('BOTTOMPADDING', (0, 0), (-1, -1), 10), + ('LEFTPADDING', (0, 0), (-1, -1), 14), + ])) + story.append(arch_box) + story.append(Spacer(1, 0.3 * cm)) + + story.append(Paragraph('Models', S['h2'])) + model_tbl = Table( + [ + [Paragraph('Model', S['table_hdr']), + Paragraph('Description', S['table_hdr'])], + [Paragraph('Llama-xLAM-2-8b-fc-r', S['table_cell']), + Paragraph('Base model — direct plain inference', S['table_cell'])], + [Paragraph('Llama-xLAM-2-8b-fc-r-encrypted', S['table_cell']), + Paragraph('Same base architecture — routed through DataKrypto TEE', S['table_cell'])], + ], + colWidths=[7 * cm, 9.5 * cm], + ) + model_tbl.setStyle(TableStyle([ + ('BACKGROUND', (0, 0), (-1, 0), colors.HexColor(C_NAVY)), + ('TEXTCOLOR', (0, 0), (-1, 0), colors.white), + ('GRID', (0, 0), (-1, -1), 0.5, colors.HexColor('#bdc3c7')), + ('ROWBACKGROUNDS',(0, 1), (-1, -1), [colors.white, colors.HexColor('#ebf5fb')]), + ('TOPPADDING', (0, 0), (-1, -1), 5), + ('BOTTOMPADDING',(0, 0), (-1, -1), 5), + ('LEFTPADDING', (0, 0), (-1, -1), 8), + ])) + story.append(model_tbl) + story.append(Spacer(1, 0.3 * cm)) + + story.append(Paragraph('Test Matrix', S['h2'])) + story.append(Paragraph( + 'Input tokens: 2,000 (fixed)  |  ' + 'Output tokens: 100 and 1,024  |  ' + 'Concurrencies: 1, 2, 4, 8, 16, 32 simultaneous HTTP requests  |  ' + 'Single Run (1 round) + Multi Run (10 rounds)', + S['body'], + )) + story.append(Paragraph( + 'Each concurrency level sends N independent HTTP requests simultaneously — ' + 'not a single batched API call. The SambaNova server dynamically batches ' + 'simultaneous requests for inference. When the TEE is in the path, its sequential ' + 'per-core processing may stagger request arrival at the server, effectively ' + 'reducing the observed batch size.', + S['body'], + )) + + story.append(PageBreak()) + + # ── SECTION 2: SERVER METRICS ──────────────────────────────────────── + story.append(section_rule()) + story.append(Paragraph('2. Server-Side Metrics — Assumption Validation', S['h1'])) + story.append(Paragraph( + 'With both SambaNova endpoints on equivalent configurations, server-side metrics directly ' + 'validate the core assumption: the TEE has no impact on inference speed inside the server. ' + 'At concurrency 1–4, both models show nearly identical server TTFT (~0.091 s) and output ' + 'tok/s (~585–620 tok/s). At higher concurrencies, the TEE de-batching effect becomes ' + 'visible: encrypted server TTFT stays flat at ~0.163 s while non-encrypted rises to ' + '~0.311 s as the server absorbs the full simultaneous batch.', + S['body'], + )) + story.append(make_chart_server()) + story.append(Paragraph( + 'Figure 1: Server TTFT p50 (top) and Server Output Tok/s p50 (bottom) for 100 and 1,024 ' + 'output tokens. Multi Run (10x). Solid = Encrypted, Dashed = Non-Encrypted.', + S['caption'], + )) + story.append(finding_box( + 'Finding: Server metrics confirm TEE adds zero inference overhead', + [ + 'At concurrency 1–4, server TTFT is ~0.091 s and server tok/s is ~585–620 tok/s ' + 'for both models — the values are virtually identical. This directly confirms that ' + 'the TEE does not slow down the SambaNova inference engine in any way.', + 'At concurrency 8–32, the TEE de-batching effect appears: encrypted server TTFT ' + 'stays at ~0.163 s while non-encrypted jumps to ~0.311 s. The TEE\'s sequential ' + 'per-core processing staggers request arrival, so the server sees a smaller effective ' + 'batch for encrypted requests. This lower server TTFT is a secondary benefit of the ' + 'TEE architecture — at high concurrency with short responses, it can more than ' + 'compensate for the TEE network overhead, making encrypted client TTFT lower than ' + 'non-encrypted at concurrency ≥ 16 (100 token responses).', + ], + S, + )) + + story.append(PageBreak()) + + # ── SECTION 3: TEE OVERHEAD ────────────────────────────────────────── + story.append(section_rule()) + story.append(Paragraph('3. TEE Overhead Decomposition', S['h1'])) + story.append(Paragraph( + 'The TEE reports three measurable latency components per request. ' + 'Understanding their relative magnitudes is key to identifying where ' + 'improvement effort should be focused.', + S['body'], + )) + + # Component table + comp_tbl = Table( + [ + [Paragraph('Component', S['table_hdr']), + Paragraph('Source', S['table_hdr']), + Paragraph('Observed Range', S['table_hdr']), + Paragraph('Grows with…', S['table_hdr'])], + [Paragraph('Input Encryption', S['table_cell']), + Paragraph('TEE encrypts the 2,000-token prompt', S['table_cell']), + Paragraph('~5–10 ms', S['table_cell']), + Paragraph('Near-constant (independent of output)', S['table_cell'])], + [Paragraph('Output Decryption', S['table_cell']), + Paragraph('TEE decrypts each streaming chunk (per token batch)', S['table_cell']), + Paragraph('~0.1–4 ms total', S['table_cell']), + Paragraph('Output token count (linear, microseconds/chunk)', S['table_cell'])], + [Paragraph('Network Hop', S['table_cell']), + Paragraph('Extra Client→TEE→Server→TEE→Client roundtrip', S['table_cell']), + Paragraph('250 ms – 6,600 ms', S['table_cell']), + Paragraph('Concurrency (queue) + Output tokens (streaming RTTs)', S['table_cell'])], + ], + colWidths=[3.2*cm, 5.0*cm, 3.3*cm, 5.0*cm], + ) + comp_tbl.setStyle(TableStyle([ + ('BACKGROUND', (0, 0), (-1, 0), colors.HexColor(C_NAVY)), + ('TEXTCOLOR', (0, 0), (-1, 0), colors.white), + ('GRID', (0, 0), (-1, -1), 0.5, colors.HexColor('#bdc3c7')), + ('ROWBACKGROUNDS',(0, 1), (-1, -1), [colors.white, colors.HexColor('#ebf5fb')]), + ('TOPPADDING', (0, 0), (-1, -1), 5), + ('BOTTOMPADDING',(0, 0), (-1, -1), 5), + ('LEFTPADDING', (0, 0), (-1, -1), 6), + ('RIGHTPADDING', (0, 0), (-1, -1), 6), + ('VALIGN', (0, 0), (-1, -1), 'MIDDLE'), + ('BACKGROUND', (0, 4), (-1, 4), colors.HexColor('#fef9e7')), # highlight network row + ])) + story.append(comp_tbl) + story.append(Spacer(1, 0.3 * cm)) + + story.append(make_chart_tee_breakdown()) + story.append(Paragraph( + 'Figure 2: TEE overhead per request broken down into Network Hop (orange), ' + 'Input Encryption (red), Output Decryption (green), and unexplained residual (purple). ' + 'Percentages inside bars = network hop as % of total encrypted client TTFT.', + S['caption'], + )) + + story.append(make_chart_crypto_times()) + story.append(Paragraph( + 'Figure 3: Encryption time (left) and decryption time (right) vs concurrency. ' + 'Solid lines = Single Run, Dashed = Multi Run. ' + 'Blue = 100 output tokens, Orange = 1,024 output tokens.', + S['caption'], + )) + + story.append(finding_box( + 'Finding: Network hop dominates — crypto overhead is negligible', + [ + 'The network hop accounts for 85–97% of the total TEE overhead in every scenario. ' + 'Input encryption is approximately constant at ~5–10 ms and decreases slightly at ' + 'higher concurrencies (likely TEE CPU sharing effects). Output decryption grows ' + 'linearly with output tokens (~0.1 ms for 100 tokens, ~3–4 ms for 1,024 tokens) ' + 'because each streaming chunk is decrypted individually — each chunk costs on the ' + 'order of microseconds.', + 'Implication: Optimising the encryption/decryption algorithm would have minimal ' + 'impact. Reducing the network roundtrip time (physical proximity of TEE to ' + 'client and to the inference server) is the highest-leverage improvement.', + ], + S, + )) + + story.append(PageBreak()) + + # ── SECTION 4: NETWORK LATENCY ─────────────────────────────────────── + story.append(section_rule()) + story.append(Paragraph('4. Network Latency Growth Pattern', S['h1'])) + story.append(Paragraph( + 'The TEE network latency grows along two dimensions: concurrency (TEE queue depth) ' + 'and output token count (more streaming chunks = more decryption RTTs).', + S['body'], + )) + story.append(make_chart_network()) + story.append(Paragraph( + 'Figure 4: TEE network latency vs concurrency for 100 output tokens (left) ' + 'and 1,024 output tokens (right). Solid = Single Run, Dashed = Multi Run. ' + 'Red vertical line marks the TEE 8-core limit.', + S['caption'], + )) + story.append(finding_box( + 'Finding: Two distinct growth regimes', + [ + 'Below TEE capacity (concurrency ≤ 8): Network latency grows moderately ' + 'because requests can be processed in parallel across available cores. ' + 'At concurrency 1 with 100 output tokens the mean network latency is ~425 ms ' + '(Multi Run); at concurrency 8 it rises to ~638 ms.', + 'Above TEE capacity (concurrency > 8): Requests queue at the TEE, ' + 'causing network latency to grow faster. At concurrency 32 with 100 output ' + 'tokens it reaches ~803 ms; for 1,024 output tokens it can exceed 3,100 ms. ' + 'This is where error rates also begin to climb.', + 'Output tokens scale the baseline: Longer responses require more ' + 'streaming decrypt RTTs. At concurrency 1, the jump from 100 to 1,024 output ' + 'tokens increases network latency from ~425 ms to ~1,608 ms (~3.8×).', + ], + S, + )) + + story.append(PageBreak()) + + # ── SECTION 5: CLIENT IMPACT ───────────────────────────────────────── + story.append(section_rule()) + story.append(Paragraph('5. Client-Side Impact — User-Facing Latency', S['h1'])) + story.append(Paragraph( + 'Client TTFT and system throughput capture the full end-user experience ' + 'including all TEE overhead. The shaded area in the TTFT chart represents ' + 'the TEE overhead gap.', + S['body'], + )) + story.append(make_chart_client()) + story.append(Paragraph( + 'Figure 5: Client TTFT p50 (top) and completed requests per minute (bottom) ' + 'for 100 and 1,024 output tokens. Multi Run (10x). ' + 'At concurrency ≥ 16 (100 tokens), encrypted TTFT drops below non-encrypted ' + 'due to the TEE de-batching benefit on server prefill time.', + S['caption'], + )) + story.append(warning_box( + 'Warning: System throughput gap widens at high concurrency (Encrypted)', + [ + 'Non-Encrypted throughput scales near-linearly with concurrency, ' + 'reaching ~848 req/min at concurrency 32 (100 output tokens). ' + 'Encrypted throughput peaks at concurrency 2 (~180 req/min) and ' + 'falls to ~28 req/min at concurrency 32 — a ~30× gap. This collapse ' + 'is driven by the TEE queue building up (>8 cores) and requests experiencing ' + 'progressively longer network waits. At concurrency >8, error rates reach ' + '11–19%, further reducing effective throughput. ' + 'Note: despite lower per-request TTFT at high concurrency for 100-token ' + 'responses, the overall system throughput (req/min) remains far lower for ' + 'the encrypted path due to TEE queueing and errors.', + ], + S, + )) + + story.append(PageBreak()) + + # ── SECTION 6: SUMMARY TABLE ───────────────────────────────────────── + story.append(section_rule()) + story.append(Paragraph('6. Summary Data Table (Multi Run)', S['h1'])) + story.append(Paragraph( + 'All latency values in seconds unless noted. ' + 'TEE % = (network + encryption + decryption) / encrypted client TTFT × 100.', + S['body'], + )) + story.append(build_summary_table(S)) + story.append(Spacer(1, 0.4 * cm)) + + story.append(PageBreak()) + + # ── SECTION 7: CONCLUSIONS ─────────────────────────────────────────── + story.append(section_rule()) + story.append(Paragraph('7. Conclusions', S['h1'])) + + conclusions = [ + ('TEE adds zero server inference overhead — confirmed. With equivalent endpoint ' + 'configurations, server TTFT and tok/s are nearly identical at concurrency 1–4 ' + '(~0.091 s and ~585–620 tok/s respectively). The TEE de-batching effect is also ' + 'confirmed: at concurrency 8–32, encrypted server TTFT stays flat at ~0.163 s while ' + 'non-encrypted rises to ~0.311 s. At concurrency ≥ 16 with short responses, this ' + 'server-side benefit can outweigh the TEE network overhead entirely.'), + ('The overhead is entirely network-driven. The extra network hop through ' + 'the TEE accounts for 85–97% of the additional client-visible latency. ' + 'Encryption (~5–10 ms) and decryption (~0.1–4 ms cumulative) are negligible.'), + ('Network latency is predictable at low concurrency. Below TEE capacity ' + '(concurrency ≤ 8), the added latency is roughly constant per round-trip and ' + 'scales linearly with output token count — consistent with the TEE team\'s claims.'), + ('TEE queue saturation is the critical scalability limit. At concurrency > 8, ' + 'the 8-core TEE begins queuing requests, causing latency to grow non-linearly ' + 'and error rates to increase. Production deployments should stay at or below ' + 'the TEE core count for reliable performance.'), + ('Per-user latency overhead at concurrency 1: Client TTFT increases by ' + '~0.19 s (100 output tokens) to ~0.17 s (1,024 output tokens) in Multi Run — ' + 'relatively modest for a confidential computing solution.'), + ] + for c in conclusions: + story.append(Paragraph(f'• {c}', S['bullet'])) + story.append(Spacer(1, 0.15 * cm)) + + story.append(Spacer(1, 0.4 * cm)) + story.append(Paragraph('Recommendations', S['h2'])) + recs = [ + 'Reduce TEE network latency: Co-locate the TEE as close as possible to both ' + 'the client and the SambaNova inference server to minimise round-trip times.', + 'Respect TEE core limit: Keep concurrent request load at or below 8 to avoid ' + 'queue saturation. Above this point latency grows non-linearly and errors appear.', + 'No need to optimise crypto: Encryption and decryption are already fast enough ' + 'that further optimisation would have negligible user-visible impact.', + 'Scale horizontally: For higher throughput with the TEE, scale the number of ' + 'TEE instances rather than increasing concurrency per instance beyond 8.', + ] + for r in recs: + story.append(Paragraph(f'• {r}', S['bullet'])) + story.append(Spacer(1, 0.1 * cm)) + + story.append(Spacer(1, 0.5 * cm)) + story.append(section_rule(color='#bdc3c7')) + story.append(Paragraph( + f'DataKrypto × SambaNova — Confidential Benchmarking Report — {dt_date.today().strftime("%B %Y")}', + ParagraphStyle('footer', fontSize=7.5, fontName='Helvetica', + textColor=colors.HexColor('#95a5a6'), alignment=TA_CENTER), + )) + + doc.build(story) + print(f'✅ Report saved to: {OUT_PDF}') + + +if __name__ == '__main__': + build_pdf() diff --git a/benchmarking/benchmarking_bundles/notebooks/datakrypto-tee-performance-analysis.ipynb b/benchmarking/benchmarking_bundles/notebooks/datakrypto-tee-performance-analysis.ipynb new file mode 100644 index 000000000..1187ddc4e --- /dev/null +++ b/benchmarking/benchmarking_bundles/notebooks/datakrypto-tee-performance-analysis.ipynb @@ -0,0 +1,27218 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "cell-setup", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setup complete\n" + ] + } + ], + "source": [ + "import os\n", + "import sys\n", + "import warnings\n", + "import numpy as np\n", + "import pandas as pd\n", + "import plotly.express as px\n", + "import plotly.graph_objects as go\n", + "from plotly.subplots import make_subplots\n", + "\n", + "warnings.filterwarnings('ignore')\n", + "pd.set_option('display.float_format', '{:.4f}'.format)\n", + "pd.set_option('display.max_columns', 50)\n", + "\n", + "for rel_path in ['../', '../../', '../../../']:\n", + " abs_path = os.path.abspath(rel_path)\n", + " if abs_path not in sys.path:\n", + " sys.path.insert(0, abs_path)\n", + "\n", + "from benchmarking.utils import read_perf_eval_json_files\n", + "\n", + "COLOR_MAP = {'Encrypted': '#EF553B', 'Non-Encrypted': '#636EFA'}\n", + "SYMBOL_MAP = {'Encrypted': 'circle', 'Non-Encrypted': 'square'}\n", + "CONC_ORDER = [1, 2, 4, 8, 16, 32]\n", + "CONC_STR = [str(c) for c in CONC_ORDER]\n", + "\n", + "print('Setup complete')" + ] + }, + { + "cell_type": "markdown", + "id": "cell-title", + "metadata": {}, + "source": [ + "# DataKrypto TEE Performance Analysis\n", + "## Encrypted vs Non-Encrypted Model — Benchmarking Report\n", + "\n", + "---\n", + "\n", + "### Architecture\n", + "\n", + "```\n", + "Non-Encrypted:\n", + " Client ──────────────────────────► SambaNova Inference Server\n", + " (1 network hop)\n", + "\n", + "Encrypted:\n", + " Client ──► DataKrypto TEE ───────► SambaNova Inference Server\n", + " │ encrypts input returns encrypted chunks\n", + " │ decrypts each output ◄─────────────────────────┘\n", + " │ chunk (streaming)\n", + " └──► Client\n", + " (2 network hops total)\n", + "```\n", + "\n", + "### Models\n", + "| Model | Description |\n", + "|-------|-------------|\n", + "| `Llama-xLAM-2-8b-fc-r` | Base model (plain inference) |\n", + "| `Llama-xLAM-2-8b-fc-r-encrypted` | Runs through DataKrypto TEE |\n", + "\n", + "> Note: The two models are served by **separate SambaNova endpoints** which may have\n", + "> different hardware allocations or decode configurations. Any baseline performance\n", + "> difference between them is an endpoint-level characteristic — not caused by the TEE.\n", + "\n", + "### Test Matrix\n", + "- **Input tokens**: 2,000 (fixed)\n", + "- **Output tokens**: 100 and 1,024\n", + "- **Concurrencies**: 1, 2, 4, 8, 16, 32\n", + "- Each concurrency level = **N separate HTTP requests sent simultaneously** (not a single batch API call — N independent requests hitting the server at the same time, which the server can dynamically batch together for inference)\n", + "- **Single Run (1x)**: One round of N simultaneous requests — clean first-hit baseline at each concurrency level\n", + "- **Multi Run (10x)**: 10 sequential rounds of the same N simultaneous requests — same per-round server load as Single Run, but repeated 10 times. Provides statistical robustness and tests sustained server/TEE behavior over time\n", + "\n", + "> Key difference: Single and Multi Run have the **same per-round concurrency load**.\n", + "> Multi Run adds statistical reliability (more samples) and reveals sustained-load behavior —\n", + "> it does not increase the number of simultaneous requests per round.\n", + "\n", + "### TEE Overhead Sources\n", + "1. **Input Encryption** — encrypts the prompt (~4–10 ms, near-constant per request)\n", + "2. **Output Decryption** — decrypts each streaming token chunk (grows with output tokens)\n", + "3. **Network Hop** — extra Client → TEE → Server roundtrip (dominant overhead)\n", + "\n", + "> **TEE capacity constraint**: The TEE has **8 cores** and processes encryption/decryption\n", + "> sequentially per core. At concurrency > 8, the TEE queue fills up causing requests to wait,\n", + "> which means concurrent requests may arrive at the inference server **staggered** rather than\n", + "> simultaneously — reducing the effective batch size the server sees.\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-load", + "metadata": {}, + "outputs": [], + "source": "DATA_ROOT = os.path.abspath('../../data/datakrypto')\n\nDATASETS = {\n 'enc_simple': {\n 'consolidated': f'{DATA_ROOT}/speed_bench_test_encrypted_simple/consolidated_results/20260225-155057.028751.xlsx',\n 'run_dir': f'{DATA_ROOT}/speed_bench_test_encrypted_simple/20260225-155057.028751',\n 'encrypted': True, \n 'run_type': 'Single Run (1x)', \n 'label': 'Encrypted',\n },\n 'plain_simple': {\n 'consolidated': f'{DATA_ROOT}/speed_bench_test_non_encrypted_simple/consolidated_results/20260227-204607.930274.xlsx',\n 'run_dir': f'{DATA_ROOT}/speed_bench_test_non_encrypted_simple/20260227-204607.930274',\n 'encrypted': False, \n 'run_type': 'Single Run (1x)', \n 'label': 'Non-Encrypted',\n },\n 'enc_multiple': {\n 'consolidated': f'{DATA_ROOT}/speed_bench_test_encrypted_multiple/consolidated_results/20260225-150147.253373.xlsx',\n 'run_dir': f'{DATA_ROOT}/speed_bench_test_encrypted_multiple/20260225-150147.253373',\n 'encrypted': True, \n 'run_type': 'Multi Run (10x)', \n 'label': 'Encrypted',\n },\n 'plain_multiple': {\n 'consolidated': f'{DATA_ROOT}/speed_bench_test_non_encrypted_multiple/consolidated_results/20260227-205436.463636.xlsx',\n 'run_dir': f'{DATA_ROOT}/speed_bench_test_non_encrypted_multiple/20260227-205436.463636',\n 'encrypted': False, \n 'run_type': 'Multi Run (10x)', \n 'label': 'Non-Encrypted',\n },\n}\n\ndfs = []\nfor key, meta in DATASETS.items():\n df = pd.read_excel(meta['consolidated'])\n df['dataset_key'] = key\n df['encrypted'] = meta['encrypted']\n df['run_type'] = meta['run_type']\n df['label'] = meta['label']\n dfs.append(df)\n\ndf_all = pd.concat(dfs, ignore_index=True)\n\n# Derived columns\ndf_all['out_tokens_label'] = df_all['num_output_tokens'].astype(str) + ' output tokens'\ndf_all['total_requests'] = df_all['num_completed_requests'] + df_all['number_errors']\ndf_all['error_rate_pct'] = (df_all['number_errors'] / df_all['total_requests'].replace(0, np.nan)) * 100\ndf_all['completion_rate_pct'] = 100 - df_all['error_rate_pct']\n\n# Ordered categorical for consistent facet ordering\ndf_all['run_type'] = pd.Categorical(\n df_all['run_type'],\n categories=['Single Run (1x)', 'Multi Run (10x)'],\n ordered=True\n)\n\nprint(f'Loaded {len(df_all)} rows across {df_all[\"dataset_key\"].nunique()} datasets')\ndf_all.groupby(['label', 'run_type'])[['num_concurrent_requests']].count().rename(columns={'num_concurrent_requests': 'scenarios'})" + }, + { + "cell_type": "markdown", + "id": "cell-overview-hdr", + "metadata": {}, + "source": [ + "## 1. Dataset Overview" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "cell-overview", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 ModelRun TypeOutput TokensConcurrencyCompletedErrorsError Rate (%)
0EncryptedSingle Run (1x)1001100.0%
1EncryptedSingle Run (1x)1002200.0%
2EncryptedSingle Run (1x)1004400.0%
3EncryptedSingle Run (1x)1008800.0%
4EncryptedSingle Run (1x)100161600.0%
5EncryptedSingle Run (1x)100323200.0%
6EncryptedSingle Run (1x)10241100.0%
7EncryptedSingle Run (1x)10242200.0%
8EncryptedSingle Run (1x)10244400.0%
9EncryptedSingle Run (1x)10248800.0%
10EncryptedSingle Run (1x)1024161600.0%
11EncryptedSingle Run (1x)1024323200.0%
12Non-EncryptedSingle Run (1x)1001100.0%
13Non-EncryptedSingle Run (1x)1002200.0%
14Non-EncryptedSingle Run (1x)1004400.0%
15Non-EncryptedSingle Run (1x)1008800.0%
16Non-EncryptedSingle Run (1x)100161600.0%
17Non-EncryptedSingle Run (1x)100323200.0%
18Non-EncryptedSingle Run (1x)10241100.0%
19Non-EncryptedSingle Run (1x)10242200.0%
20Non-EncryptedSingle Run (1x)10244400.0%
21Non-EncryptedSingle Run (1x)10248800.0%
22Non-EncryptedSingle Run (1x)1024161600.0%
23Non-EncryptedSingle Run (1x)1024323200.0%
24EncryptedMulti Run (10x)10011000.0%
25EncryptedMulti Run (10x)10022000.0%
26EncryptedMulti Run (10x)10044000.0%
27EncryptedMulti Run (10x)10088000.0%
28EncryptedMulti Run (10x)100161421811.2%
29EncryptedMulti Run (10x)100322586219.4%
30EncryptedMulti Run (10x)102411000.0%
31EncryptedMulti Run (10x)102422000.0%
32EncryptedMulti Run (10x)102444000.0%
33EncryptedMulti Run (10x)102487911.2%
34EncryptedMulti Run (10x)102416146148.8%
35EncryptedMulti Run (10x)1024322645617.5%
36Non-EncryptedMulti Run (10x)10011000.0%
37Non-EncryptedMulti Run (10x)10022000.0%
38Non-EncryptedMulti Run (10x)10044000.0%
39Non-EncryptedMulti Run (10x)10088000.0%
40Non-EncryptedMulti Run (10x)1001616000.0%
41Non-EncryptedMulti Run (10x)1003232000.0%
42Non-EncryptedMulti Run (10x)102411000.0%
43Non-EncryptedMulti Run (10x)102422000.0%
44Non-EncryptedMulti Run (10x)102444000.0%
45Non-EncryptedMulti Run (10x)102488000.0%
46Non-EncryptedMulti Run (10x)10241616000.0%
47Non-EncryptedMulti Run (10x)10243232000.0%
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cols = ['label', \n", + " 'run_type', \n", + " 'num_output_tokens', \n", + " 'num_concurrent_requests',\n", + " 'num_completed_requests', \n", + " 'number_errors', \n", + " 'error_rate_pct'\n", + " ]\n", + "overview = df_all[cols].copy()\n", + "overview.columns = ['Model', \n", + " 'Run Type', \n", + " 'Output Tokens', \n", + " 'Concurrency',\n", + " 'Completed', \n", + " 'Errors', \n", + " 'Error Rate (%)'\n", + " ]\n", + "overview['Error Rate (%)'] = overview['Error Rate (%)'].fillna(0).round(1)\n", + "overview = overview.sort_values(['Run Type', 'Model', 'Output Tokens', 'Concurrency'])\n", + "\n", + "def _highlight_errors(row):\n", + " if row['Errors'] > 0:\n", + " return ['background-color: #ffdddd'] * len(row)\n", + " return [''] * len(row)\n", + "\n", + "overview.style.apply(_highlight_errors, axis=1).format({'Error Rate (%)': '{:.1f}%'})" + ] + }, + { + "cell_type": "markdown", + "id": "cell-errors-hdr", + "metadata": {}, + "source": [ + "## 2. Error & Completion Rate Analysis\n", + "\n", + "The TEE has **8 cores** processing encryption/decryption sequentially.\n", + "At concurrency > 8, the TEE queue fills up and requests may time out or be rejected,\n", + "producing errors on the encrypted model only. The plain model should show zero errors." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cell-errors-chart", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "alignmentgroup": "True", + "hovertemplate": "Model=Encrypted
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Errors appear on Encrypted model at concurrency > 8 (TEE queue saturation). Non-Encrypted should be 0% throughout." + }, + "width": 1100, + "xaxis": { + "anchor": "y", + "categoryarray": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "categoryorder": "array", + "domain": [ + 0, + 0.48 + ], + "title": { + "text": "Concurrency" + }, + "type": "category" + }, + "xaxis2": { + "anchor": "y2", + "categoryarray": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "categoryorder": "array", + "domain": [ + 0.5, + 0.98 + ], + "matches": "x", + "title": { + "text": "Concurrency" + }, + "type": "category" + }, + "xaxis3": { + "anchor": "y3", + "domain": [ + 0, + 0.48 + ], + "matches": "x", + "showticklabels": false, + "type": "category" + }, + "xaxis4": { + "anchor": "y4", + "domain": [ + 0.5, + 0.98 + ], + "matches": "x", + "showticklabels": false, + "type": "category" + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 0.485 + ], + "title": { + "text": "Error Rate (%)" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0, + 0.485 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis3": { + "anchor": "x3", + "domain": [ + 0.515, + 1 + ], + "matches": "y", + "title": { + "text": "Error Rate (%)" + } + }, + "yaxis4": { + "anchor": "x4", + "domain": [ + 0.515, + 1 + ], + "matches": "y", + "showticklabels": false + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.bar(\n", + " df_all,\n", + " x='num_concurrent_requests',\n", + " y='error_rate_pct',\n", + " color='label',\n", + " facet_col='out_tokens_label',\n", + " facet_row='run_type',\n", + " barmode='group',\n", + " color_discrete_map=COLOR_MAP,\n", + " category_orders={\n", + " 'num_concurrent_requests': CONC_ORDER,\n", + " 'run_type': ['Single Run (1x)', 'Multi Run (10x)'],\n", + " },\n", + " labels={\n", + " 'num_concurrent_requests': 'Concurrency',\n", + " 'error_rate_pct': 'Error Rate (%)',\n", + " 'label': 'Model',\n", + " 'out_tokens_label': 'Output Tokens',\n", + " 'run_type': 'Run Type',\n", + " },\n", + " title=(\n", + " 'Request Error Rate by Concurrency
'\n", + " 'Errors appear on Encrypted model at concurrency > 8 (TEE queue saturation). '\n", + " 'Non-Encrypted should be 0% throughout.'\n", + " ),\n", + ")\n", + "fig.update_layout(height=600, width=1100, template='plotly_white')\n", + "fig.update_xaxes(type='category')\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "cell-reqmin-chart", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Model=Encrypted
Run Type=Single Run (1x)
Output Tokens=100 output tokens
Concurrency (log scale)=%{x}
Completed Requests / min=%{y}", + "legendgroup": "Encrypted", + "line": { + "color": "#EF553B", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Encrypted", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": { + "bdata": "AQIECBAg", + "dtype": "i1" + }, + "xaxis": "x3", + "y": { + "bdata": "d76fGi+dSUDFILByaE1bQBDpt68DYHBAFNBE2PB7ekBwzojS3haBQNuK/WV3nINA", + "dtype": "f8" + }, + "yaxis": "y3" + }, + { + "hovertemplate": "Model=Encrypted
Run Type=Single Run (1x)
Output Tokens=1024 output tokens
Concurrency (log scale)=%{x}
Completed Requests / min=%{y}", + "legendgroup": "Encrypted", + "line": { + "color": "#EF553B", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Encrypted", + "orientation": "v", + "showlegend": false, + "type": "scatter", + "x": { + "bdata": "AQIECBAg", + "dtype": "i1" + }, + "xaxis": "x4", + "y": { + "bdata": "CmgibHgKZUBrmnecopNLQFInoImwaUpAMlUwKqmbW0Bhw9MrZW9iQOJYF7fR+GVA", + "dtype": "f8" + }, + "yaxis": "y4" + }, + { + "hovertemplate": "Model=Encrypted
Run Type=Multi Run (10x)
Output Tokens=100 output tokens
Concurrency (log scale)=%{x}
Completed Requests / min=%{y}", + "legendgroup": "Encrypted", + "line": { + "color": "#EF553B", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Encrypted", + "orientation": "v", + "showlegend": false, + "type": "scatter", + "x": { + "bdata": "AQIECBAg", + "dtype": "i1" + }, + "xaxis": "x", + "y": { + "bdata": "RpT2Bl94W0DDZKpgVHpmQMKGp1fKoj9A3bWEfNArQkA4Z0Rpb4A9QHNoke18bzxA", + "dtype": "f8" + }, + "yaxis": "y" + }, + { + "hovertemplate": "Model=Encrypted
Run Type=Multi Run (10x)
Output Tokens=1024 output tokens
Concurrency (log scale)=%{x}
Completed Requests / min=%{y}", + "legendgroup": "Encrypted", + "line": { + "color": "#EF553B", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Encrypted", + "orientation": "v", + "showlegend": false, + "type": "scatter", + "x": { + "bdata": "AQIECBAg", + "dtype": "i1" + }, + "xaxis": "x2", + "y": { + "bdata": "ysNCrWkOQUCC4seYu3ZGQHgLJCh+fEdA2c73U+MVPkBWfa62Yr88QHBfB84ZgT5A", + "dtype": "f8" + }, + "yaxis": "y2" + }, + { + "hovertemplate": "Model=Non-Encrypted
Run Type=Single Run (1x)
Output Tokens=100 output tokens
Concurrency (log scale)=%{x}
Completed Requests / min=%{y}", + "legendgroup": "Non-Encrypted", + "line": { + "color": "#636EFA", + "dash": "solid" + }, + "marker": { + "symbol": "square" + }, + "mode": "lines+markers", + "name": "Non-Encrypted", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": { + "bdata": "AQIECBAg", + "dtype": "i1" + }, + "xaxis": "x3", + "y": { + "bdata": "cF8HzhlVWEBbsb/snnBlQLHh6ZWyO3FAGw3gLZD4fEDVCWgibNGBQN/gC5MpoYJA", + "dtype": "f8" + }, + "yaxis": "y3" + }, + { + "hovertemplate": "Model=Non-Encrypted
Run Type=Single Run (1x)
Output Tokens=1024 output tokens
Concurrency (log scale)=%{x}
Completed Requests / min=%{y}", + "legendgroup": "Non-Encrypted", + "line": { + "color": "#636EFA", + "dash": "solid" + }, + "marker": { + "symbol": "square" + }, + "mode": "lines+markers", + "name": "Non-Encrypted", + "orientation": "v", + "showlegend": false, + "type": "scatter", + "x": { + "bdata": "AQIECBAg", + "dtype": "i1" + }, + "xaxis": "x4", + "y": { + "bdata": "MQisHFpUQEA9CtejcA1MQG/whclU+VNAokW28/2AYUC5jQbwFkRnQApoImx4qG1A", + "dtype": "f8" + }, + "yaxis": "y4" + }, + { + "hovertemplate": "Model=Non-Encrypted
Run Type=Multi Run (10x)
Output Tokens=100 output tokens
Concurrency (log scale)=%{x}
Completed Requests / min=%{y}", + "legendgroup": "Non-Encrypted", + "line": { + "color": "#636EFA", + "dash": "solid" + }, + "marker": { + "symbol": "square" + }, + "mode": "lines+markers", + "name": "Non-Encrypted", + "orientation": "v", + "showlegend": false, + "type": "scatter", + "x": { + "bdata": "AQIECBAg", + "dtype": "i1" + }, + "xaxis": "x", + "y": { + "bdata": "fT81XrohY0CIhVrTvEdnQJZDi2znW3ZAeekmMYgugUCDUUmdgDqKQGHD0ytlgopA", + "dtype": "f8" + }, + "yaxis": "y" + }, + { + "hovertemplate": "Model=Non-Encrypted
Run Type=Multi Run (10x)
Output Tokens=1024 output tokens
Concurrency (log scale)=%{x}
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ndicator of system capacity. Encrypted model throughput is severely limited by TEE queueing at higher concurrencies." + }, + "width": 1100, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 0.48 + ], + "ticktext": [ + "1", + "2", + "4", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0.5, + 0.98 + ], + "matches": "x", + "ticktext": [ + "1", + "2", + "4", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis3": { + "anchor": "y3", + "domain": [ + 0, + 0.48 + ], + "matches": "x", + "showticklabels": false, + "ticktext": [ + "1", + "2", + "4", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "type": "log" + }, + "xaxis4": { + "anchor": "y4", + "domain": [ + 0.5, + 0.98 + ], + "matches": "x", + "showticklabels": false, + "ticktext": [ + "1", + "2", + "4", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "type": "log" + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 0.485 + ], + "title": { + "text": "Completed Requests / min" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0, + 0.485 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis3": { + "anchor": "x3", + "domain": [ + 0.515, + 1 + ], + "matches": "y", + "title": { + "text": "Completed Requests / min" + } + }, + "yaxis4": { + "anchor": "x4", + "domain": [ + 0.515, + 1 + ], + "matches": "y", + "showticklabels": false + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Completed requests per minute — real system throughput capacity\n", + "fig = px.line(\n", + " df_all,\n", + " x='num_concurrent_requests',\n", + " y='num_completed_requests_per_min',\n", + " color='label',\n", + " symbol='label',\n", + " facet_col='out_tokens_label',\n", + " facet_row='run_type',\n", + " color_discrete_map=COLOR_MAP,\n", + " symbol_map=SYMBOL_MAP,\n", + " log_x=True,\n", + " markers=True,\n", + " labels={\n", + " 'num_concurrent_requests': 'Concurrency (log scale)',\n", + " 'num_completed_requests_per_min': 'Completed Requests / min',\n", + " 'label': 'Model',\n", + " 'out_tokens_label': 'Output Tokens',\n", + " 'run_type': 'Run Type',\n", + " },\n", + " title=(\n", + " 'System Throughput: Completed Requests per Minute
'\n", + " 'ndicator of system capacity. '\n", + " 'Encrypted model throughput is severely limited by TEE queueing at higher concurrencies.'\n", + " ),\n", + ")\n", + "fig.update_layout(height=700, width=1100, template='plotly_white')\n", + "fig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cell-server-hdr", + "metadata": {}, + "source": "## 3. Server-Side Metrics — Assumption Validation ✅\n\nWith both endpoints on equivalent configurations, server-side metrics now confirm the core assumption:\n**the TEE does not affect inference speed inside the SambaNova engine.**\n\n**Server tok/s — endpoints are now matched:**\n- Both endpoints baseline at ~585–620 tok/s (ratio 1.02–1.06× at concurrency 1–2 and 8+)\n- A small anomaly remains at concurrency 4 (ratio ~1.33–1.46×) which may be a transient batching artifact\n- The previous 1.87× gap was an endpoint configuration issue, now resolved\n\n**Server TTFT — two distinct regimes clearly visible:**\n- **Concurrency 1–4**: Both models show nearly identical server TTFT (~0.091 s and ~0.161 s respectively)\n — direct confirmation that the TEE has no impact on server prefill latency\n- **Concurrency 8–32**: Encrypted stays flat at ~0.163 s; non-encrypted jumps to ~0.311 s.\n This divergence is the **TEE de-batching effect** in action: because the TEE processes\n requests sequentially per core, they arrive at the server staggered rather than simultaneously,\n keeping the effective server batch smaller and prefill latency lower for the encrypted path.\n\n**Interesting consequence at high concurrency:** The encrypted model's server-side advantage from\nde-batching (lower TTFT) can exceed the TEE network overhead, making encrypted client TTFT\nactually lower than non-encrypted at concurrency ≥ 16 for 100-token responses (see Section 5)." + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-server-ttft", + "metadata": {}, + "outputs": [], + "source": "fig = px.line(\n df_all,\n x='num_concurrent_requests',\n y='server_ttft_s_p50',\n color='label',\n symbol='label',\n facet_col='out_tokens_label',\n facet_row='run_type',\n color_discrete_map=COLOR_MAP,\n symbol_map=SYMBOL_MAP,\n log_x=True,\n markers=True,\n labels={\n 'num_concurrent_requests': 'Concurrency (log scale)',\n 'server_ttft_s_p50': 'Server TTFT p50 (s)',\n 'label': 'Model',\n 'out_tokens_label': 'Output Tokens',\n 'run_type': 'Run Type',\n },\n title=(\n 'Server Time to First Token — p50
'\n 'Concurrency 1–4: nearly identical for both models ✅ — confirms TEE has no server-side impact. '\n 'Concurrency 8–32: encrypted stays flat (~0.163 s), non-encrypted rises to ~0.311 s. '\n 'This split is the TEE de-batching effect: staggered arrival keeps the effective server batch '\n 'smaller for the encrypted path, reducing prefill queue time.'\n ),\n)\nfig.update_layout(height=700, width=1100, template='plotly_white')\nfig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\nfig.show()" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-server-toks", + "metadata": {}, + "outputs": [], + "source": "fig = px.line(\n df_all,\n x='num_concurrent_requests',\n y='server_output_token_per_s_p50',\n color='label',\n symbol='label',\n facet_col='out_tokens_label',\n facet_row='run_type',\n color_discrete_map=COLOR_MAP,\n symbol_map=SYMBOL_MAP,\n log_x=True,\n markers=True,\n labels={\n 'num_concurrent_requests': 'Concurrency (log scale)',\n 'server_output_token_per_s_p50': 'Server Output Tok/s p50 (per request)',\n 'label': 'Model',\n 'out_tokens_label': 'Output Tokens',\n 'run_type': 'Run Type',\n },\n title=(\n 'Server Output Throughput per Request — p50
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 MetricRun TypeOutput TokensConcurrencyNon-EncryptedEncryptedDelta% Change
58Server E2E p50 (s)Multi Run (10x)10010.25090.2604+0.0095+3.8%
59Server E2E p50 (s)Multi Run (10x)10020.25110.2592+0.0081+3.2%
60Server E2E p50 (s)Multi Run (10x)10040.28810.2616-0.0265-9.2%
61Server E2E p50 (s)Multi Run (10x)10080.47400.2910-0.1830-38.6%
62Server E2E p50 (s)Multi Run (10x)100160.47430.2936-0.1807-38.1%
63Server E2E p50 (s)Multi Run (10x)100320.47520.2927-0.1825-38.4%
64Server E2E p50 (s)Multi Run (10x)102411.73951.8069+0.0674+3.9%
65Server E2E p50 (s)Multi Run (10x)102421.73991.7854+0.0455+2.6%
66Server E2E p50 (s)Multi Run (10x)102441.03691.5503+0.5134+49.5%
67Server E2E p50 (s)Multi Run (10x)102481.42851.4902+0.0617+4.3%
68Server E2E p50 (s)Multi Run (10x)1024161.42911.7507+0.3216+22.5%
69Server E2E p50 (s)Multi Run (10x)1024321.42781.6218+0.1940+13.6%
46Server E2E p50 (s)Single Run (1x)10010.25140.0939-0.1575-62.6%
47Server E2E p50 (s)Single Run (1x)10020.25070.2614+0.0107+4.3%
48Server E2E p50 (s)Single Run (1x)10040.29040.2763-0.0141-4.9%
49Server E2E p50 (s)Single Run (1x)10080.47850.4460-0.0325-6.8%
50Server E2E p50 (s)Single Run (1x)100160.47640.3979-0.0785-16.5%
51Server E2E p50 (s)Single Run (1x)100320.47750.4806+0.0031+0.6%
52Server E2E p50 (s)Single Run (1x)102411.73850.0929-1.6456-94.7%
53Server E2E p50 (s)Single Run (1x)102422.00050.9159-1.0846-54.2%
54Server E2E p50 (s)Single Run (1x)102441.03611.9133+0.8772+84.7%
55Server E2E p50 (s)Single Run (1x)102481.42691.7517+0.3248+22.8%
56Server E2E p50 (s)Single Run (1x)1024161.43211.8821+0.4500+31.4%
57Server E2E p50 (s)Single Run (1x)1024321.42781.7688+0.3410+23.9%
12Server TTFT p50 (s)Multi Run (10x)10010.09110.0910-0.0001-0.1%
13Server TTFT p50 (s)Multi Run (10x)10020.09120.0902-0.0010-1.1%
14Server TTFT p50 (s)Multi Run (10x)10040.16080.1615+0.0007+0.4%
15Server TTFT p50 (s)Multi Run (10x)10080.31080.1619-0.1489-47.9%
16Server TTFT p50 (s)Multi Run (10x)100160.31100.1646-0.1464-47.1%
17Server TTFT p50 (s)Multi Run (10x)100320.31170.1636-0.1481-47.5%
18Server TTFT p50 (s)Multi Run (10x)102410.09180.0913-0.0005-0.5%
19Server TTFT p50 (s)Multi Run (10x)102420.09210.0917-0.0004-0.4%
20Server TTFT p50 (s)Multi Run (10x)102440.16060.1632+0.0026+1.6%
21Server TTFT p50 (s)Multi Run (10x)102480.31160.1605-0.1511-48.5%
22Server TTFT p50 (s)Multi Run (10x)1024160.31150.3108-0.0007-0.2%
23Server TTFT p50 (s)Multi Run (10x)1024320.31060.1666-0.1440-46.4%
0Server TTFT p50 (s)Single Run (1x)10010.09130.0928+0.0015+1.6%
1Server TTFT p50 (s)Single Run (1x)10020.09080.0917+0.0009+1.0%
2Server TTFT p50 (s)Single Run (1x)10040.16300.1623-0.0007-0.4%
3Server TTFT p50 (s)Single Run (1x)10080.31590.3127-0.0032-1.0%
4Server TTFT p50 (s)Single Run (1x)100160.31370.3135-0.0002-0.1%
5Server TTFT p50 (s)Single Run (1x)100320.31400.3137-0.0003-0.1%
6Server TTFT p50 (s)Single Run (1x)102410.09100.0917+0.0007+0.8%
7Server TTFT p50 (s)Single Run (1x)102420.16820.0912-0.0770-45.8%
8Server TTFT p50 (s)Single Run (1x)102440.15970.1310-0.0287-18.0%
9Server TTFT p50 (s)Single Run (1x)102480.31130.3144+0.0031+1.0%
10Server TTFT p50 (s)Single Run (1x)1024160.31290.3136+0.0007+0.2%
11Server TTFT p50 (s)Single Run (1x)1024320.31080.3143+0.0035+1.1%
34Server Tok/s p50Multi Run (10x)1001619.4161584.8025-34.6136-5.6%
35Server Tok/s p50Multi Run (10x)1002619.3149584.7465-34.5684-5.6%
36Server Tok/s p50Multi Run (10x)1004776.2750585.0394-191.2356-24.6%
37Server Tok/s p50Multi Run (10x)1008607.8911584.4519-23.4392-3.9%
38Server Tok/s p50Multi Run (10x)10016605.6483584.3005-21.3478-3.5%
39Server Tok/s p50Multi Run (10x)10032605.8772593.5173-12.3599-2.0%
40Server Tok/s p50Multi Run (10x)10241620.9225585.0422-35.8803-5.8%
41Server Tok/s p50Multi Run (10x)10242620.7307585.1295-35.6012-5.7%
42Server Tok/s p50Multi Run (10x)10244784.1768538.2166-245.9602-31.4%
43Server Tok/s p50Multi Run (10x)10248615.3324585.2877-30.0447-4.9%
44Server Tok/s p50Multi Run (10x)102416614.8655587.5843-27.2812-4.4%
45Server Tok/s p50Multi Run (10x)102432614.8678589.2794-25.5884-4.2%
24Server Tok/s p50Single Run (1x)1002619.2327583.6140-35.6187-5.8%
25Server Tok/s p50Single Run (1x)1004776.9642765.2556-11.7086-1.5%
26Server Tok/s p50Single Run (1x)1008609.0278588.3291-20.6987-3.4%
27Server Tok/s p50Single Run (1x)10016608.3037595.5981-12.7056-2.1%
28Server Tok/s p50Single Run (1x)10032605.6461594.1293-11.5168-1.9%
29Server Tok/s p50Single Run (1x)10242558.3128545.0032-13.3096-2.4%
30Server Tok/s p50Single Run (1x)10244783.9373527.9832-255.9541-32.6%
31Server Tok/s p50Single Run (1x)10248615.8302581.3243-34.5059-5.6%
32Server Tok/s p50Single Run (1x)102416615.4362593.4667-21.9695-3.6%
33Server Tok/s p50Single Run (1x)102432615.4209593.0451-22.3758-3.6%
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Server metrics delta table — Encrypted minus Non-Encrypted\n", + "DIMS = ['run_type', 'num_output_tokens', 'num_concurrent_requests']\n", + "\n", + "df_enc_s = df_all[df_all['label'] == 'Encrypted'].set_index(DIMS)\n", + "df_plain_s = df_all[df_all['label'] == 'Non-Encrypted'].set_index(DIMS)\n", + "\n", + "server_metrics = [\n", + " ('server_ttft_s_p50', 'Server TTFT p50 (s)'),\n", + " ('server_output_token_per_s_p50', 'Server Tok/s p50'),\n", + " ('server_end_to_end_latency_s_p50','Server E2E p50 (s)'),\n", + "]\n", + "\n", + "delta_rows = []\n", + "for col, label in server_metrics:\n", + " if col not in df_enc_s.columns:\n", + " continue\n", + " for idx in df_enc_s.index:\n", + " if idx not in df_plain_s.index:\n", + " continue\n", + " enc_val = df_enc_s.loc[idx, col]\n", + " plain_val = df_plain_s.loc[idx, col]\n", + " if pd.isna(enc_val) or pd.isna(plain_val):\n", + " continue\n", + " delta = enc_val - plain_val\n", + " pct_chg = delta / plain_val * 100 if plain_val != 0 else np.nan\n", + " delta_rows.append({\n", + " 'Metric': label,\n", + " 'Run Type': idx[0],\n", + " 'Output Tokens': idx[1],\n", + " 'Concurrency': idx[2],\n", + " 'Non-Encrypted': plain_val,\n", + " 'Encrypted': enc_val,\n", + " 'Delta': delta,\n", + " '% Change': pct_chg,\n", + " })\n", + "\n", + "df_server_delta = pd.DataFrame(delta_rows)\n", + "df_server_delta = df_server_delta.sort_values(['Metric', 'Run Type', 'Output Tokens', 'Concurrency'])\n", + "\n", + "def _pct_color(val):\n", + " if pd.isna(val): return ''\n", + " if val > 50: return 'background-color: #ffaaaa'\n", + " if val > 20: return 'background-color: #ffd8aa'\n", + " if val > 0: return 'background-color: #fffacc'\n", + " if val < -20: return 'background-color: #aaffaa'\n", + " return ''\n", + "\n", + "df_server_delta.style \\\n", + " .applymap(_pct_color, subset=['% Change']) \\\n", + " .format({\n", + " 'Non-Encrypted': '{:.4f}',\n", + " 'Encrypted': '{:.4f}',\n", + " 'Delta': '{:+.4f}',\n", + " '% Change': '{:+.1f}%',\n", + " })" + ] + }, + { + "cell_type": "markdown", + "id": "cell-tee-hdr", + "metadata": {}, + "source": [ + "## 4. TEE Overhead Decomposition (Encrypted Model Only)\n", + "\n", + "Three measurable components are returned by the TEE per request:\n", + "\n", + "| Component | Description | Expected Behavior |\n", + "|-----------|-------------|-------------------|\n", + "| **Input Encryption** | Time to encrypt the prompt before sending to server | ~constant, independent of output length |\n", + "| **Output Decryption** | Cumulative time to decrypt all streaming output chunks | grows with output token count |\n", + "| **Network Hop** | Round-trip latency through the TEE (Client→TEE→Server→TEE→Client) | grows with concurrency (TEE queue) and output tokens |\n", + "\n", + "> The network hop is the dominant overhead. At high concurrency it grows due to TEE core saturation." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "cell-tee-stack", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Component=Network Hop
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Output Tokens=100 output tokens
Concurrency=%{x}
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Run Type=Single Run (1x)
Output Tokens=1024 output tokens
Concurrency=%{x}
Overhead (s)=%{y}", + "legendgroup": "Network Hop", + "marker": { + "color": "#FF7F0E", + "pattern": { + "shape": "" + } + }, + "name": "Network Hop", + "orientation": "v", + "showlegend": false, + "textposition": "auto", + "type": "bar", + "x": { + "bdata": "AQIECBAg", + "dtype": "i1" + }, + "xaxis": "x4", + "y": { + "bdata": "fv0QGywc0D+ZG8LnPeT9P/tTT4tmKgNAMJXIT/TaCUBQr+8e+6wRQPMKnwg9ZhpA", + "dtype": "f8" + }, + "yaxis": "y4" + }, + { + "hovertemplate": "Component=Input Encryption
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Output Tokens=100 output tokens
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Overhead (s)=%{y}", + "legendgroup": "Input Encryption", + "marker": { + "color": "#D62728", + "pattern": { + "shape": "" + } + }, + "name": "Input Encryption", + "orientation": "v", + "showlegend": true, + "textposition": "auto", + "type": "bar", + "x": { + "bdata": "AQIECBAg", + "dtype": "i1" + }, + "xaxis": "x3", + "y": { + "bdata": "EHLe/8cJcz9bVNUVfU92PyZ/QRw1cHE/fuNrzywJcD9TZ6SUxh5wP6yn+6Xqw28/", + "dtype": "f8" + }, + "yaxis": "y3" + }, + { + "hovertemplate": "Component=Input Encryption
Run Type=Single Run (1x)
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Network Hop dominates. Encryption is near-constant. Decryption scales with output token count (streaming per-chunk decryption)." + }, + "width": 1100, + "xaxis": { + "anchor": "y", + "categoryarray": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "categoryorder": "array", + "domain": [ + 0, + 0.48 + ], + "title": { + "text": "Concurrency" + }, + "type": "category" + }, + "xaxis2": { + "anchor": "y2", + "categoryarray": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "categoryorder": "array", + "domain": [ + 0.5, + 0.98 + ], + "matches": "x", + "title": { + "text": "Concurrency" + }, + "type": "category" + }, + "xaxis3": { + "anchor": "y3", + "domain": [ + 0, + 0.48 + ], + "matches": "x", + "showticklabels": false, + "type": "category" + }, + "xaxis4": { + "anchor": "y4", + "domain": [ + 0.5, + 0.98 + ], + "matches": "x", + "showticklabels": false, + "type": "category" + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 0.485 + ], + "title": { + "text": "Overhead (s)" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0, + 0.485 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis3": { + "anchor": "x3", + "domain": [ + 0.515, + 1 + ], + "matches": "y", + "title": { + "text": "Overhead (s)" + } + }, + "yaxis4": { + "anchor": "x4", + "domain": [ + 0.515, + 1 + ], + "matches": "y", + "showticklabels": false + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_tee = df_all[df_all['label'] == 'Encrypted'].copy()\n", + "\n", + "# Convert ms → s for consistent units with latency charts\n", + "df_tee['Network Hop'] = df_tee['server_network_latency_ms_mean'] / 1000\n", + "df_tee['Input Encryption'] = df_tee['total_encryption_time_ms_mean'] / 1000\n", + "df_tee['Output Decryption'] = df_tee['total_decryption_time_ms_mean'] / 1000\n", + "\n", + "TEE_ID_COLS = ['run_type', 'out_tokens_label', 'num_concurrent_requests']\n", + "TEE_VAL_COLS = ['Network Hop', 'Input Encryption', 'Output Decryption']\n", + "\n", + "df_tee_melt = df_tee[TEE_ID_COLS + TEE_VAL_COLS].melt(\n", + " id_vars=TEE_ID_COLS, var_name='Component', value_name='Overhead (s)'\n", + ")\n", + "\n", + "COMP_COLORS = {\n", + " 'Network Hop': '#FF7F0E',\n", + " 'Input Encryption': '#D62728',\n", + " 'Output Decryption': '#2CA02C',\n", + "}\n", + "\n", + "fig = px.bar(\n", + " df_tee_melt,\n", + " x='num_concurrent_requests',\n", + " y='Overhead (s)',\n", + " color='Component',\n", + " facet_col='out_tokens_label',\n", + " facet_row='run_type',\n", + " barmode='stack',\n", + " color_discrete_map=COMP_COLORS,\n", + " category_orders={\n", + " 'num_concurrent_requests': CONC_ORDER,\n", + " 'run_type': ['Single Run (1x)', 'Multi Run (10x)'],\n", + " },\n", + " labels={\n", + " 'num_concurrent_requests': 'Concurrency',\n", + " 'run_type': 'Run Type',\n", + " 'out_tokens_label': 'Output Tokens',\n", + " },\n", + " title=(\n", + " 'TEE Overhead Breakdown per Request (Encrypted Model Only)
'\n", + " 'Network Hop dominates. Encryption is near-constant. '\n", + " 'Decryption scales with output token count (streaming per-chunk decryption).'\n", + " ),\n", + ")\n", + "fig.update_layout(height=700, width=1100, template='plotly_white')\n", + "fig.update_xaxes(type='category')\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "cell-network-latency", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Output Tokens=100 output tokens
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Solid = Single Run (1x) | Dashed = Multi Run (10x). Grows with concurrency (TEE queueing) AND with output tokens (streaming: each decrypted chunk adds a network round-trip)." + }, + "width": 1100, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 0.49 + ], + "ticktext": [ + "1", + "2", + "4", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0.51, + 1 + ], + "matches": "x", + "ticktext": [ + "1", + "2", + "4", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "TEE Network Latency — Mean (ms)" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Network latency growth — most important overhead component\n", + "fig = px.line(\n", + " df_tee,\n", + " x='num_concurrent_requests',\n", + " y='server_network_latency_ms_mean',\n", + " color='out_tokens_label',\n", + " symbol='run_type',\n", + " line_dash='run_type',\n", + " line_dash_map={'Single Run (1x)': 'solid', 'Multi Run (10x)': 'dash'},\n", + " facet_col='out_tokens_label',\n", + " log_x=True,\n", + " markers=True,\n", + " labels={\n", + " 'num_concurrent_requests': 'Concurrency (log scale)',\n", + " 'server_network_latency_ms_mean': 'TEE Network Latency — Mean (ms)',\n", + " 'out_tokens_label': 'Output Tokens',\n", + " 'run_type': 'Run Type',\n", + " },\n", + " title=(\n", + " 'TEE Network Latency vs Concurrency
'\n", + " 'Solid = Single Run (1x) | Dashed = Multi Run (10x). '\n", + " 'Grows with concurrency (TEE queueing) AND with output tokens '\n", + " '(streaming: each decrypted chunk adds a network round-trip).'\n", + " ),\n", + ")\n", + "fig.update_layout(height=450, width=1100, template='plotly_white')\n", + "fig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "cell-enc-dec-times", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "legendgroup": "100 out | Single Run (1x)", + "line": { + "color": "#1f77b4", + "dash": "solid" + }, + "mode": "lines+markers", + "name": "100 out | Single Run (1x)", + "showlegend": true, + "type": "scatter", + "x": { + "bdata": "AQIECBAg", + "dtype": "i1" + }, + "xaxis": "x", + "y": { + "bdata": "ZDvfT42XEkBhVFInoMkVQCv2l92TBxFAUrgehetRD0DOGVHaG3wPQL7BFyZTBQ9A", + "dtype": "f8" + }, + "yaxis": "y" + }, + { + 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Encryption is near-constant (~4-10 ms). Decryption grows with output token count; decreases at higher concurrency as TEE queue pressure compresses per-request wall time." + }, + "width": 1100, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 0.45 + ], + "ticktext": [ + "1", + "2", + "4", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0.55, + 1 + ], + "ticktext": [ + "1", + "2", + "4", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Time (ms)" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Time (ms)" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Encryption and decryption times detail\n", + "fig = make_subplots(\n", + " rows=1, cols=2,\n", + " subplot_titles=['Input Encryption Time (ms)', 'Output Decryption Time (ms)'],\n", + ")\n", + "\n", + "_run_styles = {'Single Run (1x)': 'solid', 'Multi Run (10x)': 'dash'}\n", + "_tok_colors = {100: '#1f77b4', 1024: '#ff7f0e'}\n", + "_shown = set()\n", + "\n", + "for run_type, dash in _run_styles.items():\n", + " for out_tok, color in _tok_colors.items():\n", + " sub = df_tee[(df_tee['run_type'] == run_type) & (df_tee['num_output_tokens'] == out_tok)]\n", + " name = f'{out_tok} out | {run_type}'\n", + " show = name not in _shown\n", + " kw = dict(x=sub['num_concurrent_requests'], mode='lines+markers',\n", + " name=name, line=dict(color=color, dash=dash),\n", + " legendgroup=name, showlegend=show)\n", + " fig.add_trace(go.Scatter(y=sub['total_encryption_time_ms_mean'], **kw), row=1, col=1)\n", + " kw['showlegend'] = False\n", + " fig.add_trace(go.Scatter(y=sub['total_decryption_time_ms_mean'], **kw), row=1, col=2)\n", + " _shown.add(name)\n", + "\n", + "fig.update_xaxes(\n", + " type='log', tickvals=CONC_ORDER, ticktext=CONC_STR,\n", + " title_text='Concurrency (log scale)',\n", + ")\n", + "fig.update_yaxes(title_text='Time (ms)', col=1)\n", + "fig.update_yaxes(title_text='Time (ms)', col=2)\n", + "fig.update_layout(\n", + " height=420, width=1100, template='plotly_white',\n", + " legend_title='out tokens | run type',\n", + " title=(\n", + " 'TEE Encryption & Decryption Times
'\n", + " 'Encryption is near-constant (~4-10 ms). '\n", + " 'Decryption grows with output token count; decreases at higher concurrency '\n", + " 'as TEE queue pressure compresses per-request wall time.'\n", + " ),\n", + ")\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cell-client-hdr", + "metadata": {}, + "source": [ + "## 5. Client-Side Metrics — End-User Experience\n", + "\n", + "Client metrics include the full roundtrip: **Client → (TEE) → Server → (TEE) → Client**.\n", + "\n", + "The gap between encrypted and non-encrypted client metrics is the\n", + "**observable user-facing cost** of the TEE encryption layer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-client-ttft", + "metadata": {}, + "outputs": [], + "source": "fig = px.line(\n df_all,\n x='num_concurrent_requests',\n y='client_ttft_s_p50',\n color='label',\n symbol='label',\n facet_col='out_tokens_label',\n facet_row='run_type',\n color_discrete_map=COLOR_MAP,\n symbol_map=SYMBOL_MAP,\n log_x=True,\n markers=True,\n labels={\n 'num_concurrent_requests': 'Concurrency (log scale)',\n 'client_ttft_s_p50': 'Client TTFT p50 (s)',\n 'label': 'Model',\n 'out_tokens_label': 'Output Tokens',\n 'run_type': 'Run Type',\n },\n title=(\n 'Client Time to First Token — p50
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Stacked bars = measured TEE components. Total bar height = Enc TTFT − Plain TTFT. Residual = delta not fully explained by TEE metrics (measurement noise / base model diff)." + }, + "width": 1100, + "xaxis": { + "anchor": "y", + "categoryarray": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "categoryorder": "array", + "domain": [ + 0, + 0.48 + ], + "title": { + "text": "Concurrency" + }, + "type": "category" + }, + "xaxis2": { + "anchor": "y2", + "categoryarray": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "categoryorder": "array", + "domain": [ + 0.5, + 0.98 + ], + "matches": "x", + "title": { + "text": "Concurrency" + }, + "type": "category" + }, + "xaxis3": { + "anchor": "y3", + "domain": [ + 0, + 0.48 + ], + "matches": "x", + "showticklabels": false, + "type": "category" + }, + "xaxis4": { + "anchor": "y4", + "domain": [ + 0.5, + 0.98 + ], + "matches": "x", + "showticklabels": false, + "type": "category" + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 0.485 + ], + "title": { + "text": "Overhead (s)" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0, + 0.485 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis3": { + "anchor": "x3", + "domain": [ + 0.515, + 1 + ], + "matches": "y", + "title": { + "text": "Overhead (s)" + } + }, + "yaxis4": { + "anchor": "x4", + "domain": [ + 0.515, + 1 + ], + "matches": "y", + "showticklabels": false + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Client TTFT overhead attribution: stacked TEE components vs total delta\n", + "DIMS = ['run_type', 'num_output_tokens', 'num_concurrent_requests']\n", + "\n", + "df_enc_a = df_all[df_all['label'] == 'Encrypted'].copy()\n", + "df_plain_a = df_all[df_all['label'] == 'Non-Encrypted'][DIMS + ['client_ttft_s_p50']].rename(\n", + " columns={'client_ttft_s_p50': 'client_ttft_plain'}\n", + ")\n", + "df_attr = df_enc_a.merge(df_plain_a, on=DIMS)\n", + "\n", + "df_attr['client_ttft_delta'] = df_attr['client_ttft_s_p50'] - df_attr['client_ttft_plain']\n", + "df_attr['Network Hop (s)'] = df_attr['server_network_latency_ms_mean'] / 1000\n", + "df_attr['Input Encryption (s)']= df_attr['total_encryption_time_ms_mean'] / 1000\n", + "df_attr['Output Decryption (s)']= df_attr['total_decryption_time_ms_mean'] / 1000\n", + "df_attr['Residual (s)'] = (\n", + " df_attr['client_ttft_delta']\n", + " - df_attr['Network Hop (s)']\n", + " - df_attr['Input Encryption (s)']\n", + " - df_attr['Output Decryption (s)']\n", + ").clip(lower=0)\n", + "df_attr['out_tokens_label'] = df_attr['num_output_tokens'].astype(str) + ' output tokens'\n", + "\n", + "ATTR_COLS = ['Network Hop (s)', 'Input Encryption (s)', 'Output Decryption (s)', 'Residual (s)']\n", + "ATTR_COLORS = {\n", + " 'Network Hop (s)': '#FF7F0E',\n", + " 'Input Encryption (s)': '#D62728',\n", + " 'Output Decryption (s)':'#2CA02C',\n", + " 'Residual (s)': '#9467BD',\n", + "}\n", + "\n", + "df_attr_melt = df_attr.melt(\n", + " id_vars=['run_type', 'out_tokens_label', 'num_concurrent_requests', 'client_ttft_delta'],\n", + " value_vars=ATTR_COLS, var_name='Component', value_name='Overhead (s)',\n", + ").dropna(subset=['Overhead (s)'])\n", + "\n", + "fig = px.bar(\n", + " df_attr_melt,\n", + " x='num_concurrent_requests',\n", + " y='Overhead (s)',\n", + " color='Component',\n", + " facet_col='out_tokens_label',\n", + " facet_row='run_type',\n", + " barmode='stack',\n", + " color_discrete_map=ATTR_COLORS,\n", + " category_orders={\n", + " 'num_concurrent_requests': CONC_ORDER,\n", + " 'run_type': ['Single Run (1x)', 'Multi Run (10x)'],\n", + " },\n", + " labels={\n", + " 'num_concurrent_requests': 'Concurrency',\n", + " 'run_type': 'Run Type',\n", + " 'out_tokens_label': 'Output Tokens',\n", + " },\n", + " title=(\n", + " 'Client TTFT Overhead Attribution: Encrypted − Non-Encrypted Delta
'\n", + " 'Stacked bars = measured TEE components. Total bar height = Enc TTFT − Plain TTFT. '\n", + " 'Residual = delta not fully explained by TEE metrics (measurement noise / base model diff).'\n", + " ),\n", + ")\n", + "fig.update_layout(height=700, width=1100, template='plotly_white')\n", + "fig.update_xaxes(type='category')\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "cell-client-e2e", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Model=Encrypted
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Full request duration as observed by the client. Grows faster for encrypted at high concurrency due to TEE queueing." + }, + "width": 1100, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 0.48 + ], + "ticktext": [ + "1", + "2", + "4", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0.5, + 0.98 + ], + "matches": "x", + "ticktext": [ + "1", + "2", + "4", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis3": { + "anchor": "y3", + "domain": [ + 0, + 0.48 + ], + "matches": "x", + "showticklabels": false, + "ticktext": [ + "1", + "2", + "4", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "type": "log" + }, + "xaxis4": { + "anchor": "y4", + "domain": [ + 0.5, + 0.98 + ], + "matches": "x", + "showticklabels": false, + "ticktext": [ + "1", + "2", + "4", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "type": "log" + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 0.485 + ], + "title": { + "text": "Client E2E Latency p50 (s)" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0, + 0.485 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis3": { + "anchor": "x3", + "domain": [ + 0.515, + 1 + ], + "matches": "y", + "title": { + "text": "Client E2E Latency p50 (s)" + } + }, + "yaxis4": { + "anchor": "x4", + "domain": [ + 0.515, + 1 + ], + "matches": "y", + "showticklabels": false + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.line(\n", + " df_all,\n", + " x='num_concurrent_requests',\n", + " y='client_end_to_end_latency_s_p50',\n", + " color='label',\n", + " symbol='label',\n", + " facet_col='out_tokens_label',\n", + " facet_row='run_type',\n", + " color_discrete_map=COLOR_MAP,\n", + " symbol_map=SYMBOL_MAP,\n", + " log_x=True,\n", + " markers=True,\n", + " labels={\n", + " 'num_concurrent_requests': 'Concurrency (log scale)',\n", + " 'client_end_to_end_latency_s_p50': 'Client E2E Latency p50 (s)',\n", + " 'label': 'Model',\n", + " 'out_tokens_label': 'Output Tokens',\n", + " 'run_type': 'Run Type',\n", + " },\n", + " title=(\n", + " 'Client End-to-End Latency — p50
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Output Tokens=100 output tokens
Concurrency (log scale)=%{x}
Client Output Tok/s p50 (per request)=%{y}", + "legendgroup": "Encrypted", + "line": { + "color": "#EF553B", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Encrypted", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": { + "bdata": "AQIECBAg", + "dtype": "i1" + }, + "xaxis": "x3", + "y": { + "bdata": "AAAAAAAA+H+SXP5D+q6CQM4ZUdobk4hAoBov3SSRgkBI4XoUrneDQIbJVMGo5YJA", + "dtype": "f8" + }, + "yaxis": "y3" + }, + { + "hovertemplate": "Model=Encrypted
Run Type=Single Run (1x)
Output Tokens=1024 output tokens
Concurrency (log scale)=%{x}
Client Output Tok/s p50 (per request)=%{y}", + "legendgroup": "Encrypted", + "line": { + "color": "#EF553B", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Encrypted", + "orientation": "v", + "showlegend": false, + "type": "scatter", + "x": { + "bdata": "AQIECBAg", + "dtype": "i1" + }, + "xaxis": "x4", + "y": { + "bdata": "AAAAAAAA+H9lqmBUUjiBQIZa07xjhIBAjNtoAG85gkBPHhZqzZKCQEJg5dCij4JA", + "dtype": "f8" + }, + "yaxis": "y4" + }, + { + "hovertemplate": "Model=Encrypted
Run Type=Multi Run (10x)
Output Tokens=100 output tokens
Concurrency (log scale)=%{x}
Client Output Tok/s p50 (per request)=%{y}", + "legendgroup": "Encrypted", + "line": { + "color": "#EF553B", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Encrypted", + "orientation": "v", + "showlegend": false, + "type": "scatter", + "x": { + "bdata": "AQIECBAg", + "dtype": "i1" + }, + "xaxis": "x", + "y": { + "bdata": "nDOitDdxgkCVZYhj3YWCQBE2PL1SiYJAEoPAyqGKgkDLoUW2c5GCQMdLN4lBBINA", + "dtype": "f8" + }, + "yaxis": "y" + }, + { + "hovertemplate": "Model=Encrypted
Run Type=Multi Run (10x)
Output Tokens=1024 output tokens
Concurrency (log scale)=%{x}
Client Output Tok/s p50 (per request)=%{y}", + "legendgroup": "Encrypted", + "line": { + "color": "#EF553B", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Encrypted", + "orientation": "v", + "showlegend": false, + "type": "scatter", + "x": { + "bdata": "AQIECBAg", + "dtype": "i1" + }, + "xaxis": "x2", + "y": { + "bdata": "OwFNhI1FgkBJnYAmQkyCQPW52op9RoJAgy9MpopjgkDcRgN4i2aCQMUgsHLofoJA", + "dtype": "f8" + }, + "yaxis": "y2" + }, + { + "hovertemplate": "Model=Non-Encrypted
Run Type=Single Run (1x)
Output Tokens=100 output tokens
Concurrency (log scale)=%{x}
Client Output Tok/s p50 (per request)=%{y}", + "legendgroup": "Non-Encrypted", + "line": { + "color": "#636EFA", + "dash": "solid" + }, + "marker": { + "symbol": "square" + }, + "mode": "lines+markers", + "name": "Non-Encrypted", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": { + "bdata": "AQIECBAg", + "dtype": "i1" + }, + "xaxis": "x3", + "y": { + "bdata": "zF1LyAeAg0C3Yn/ZvaeDQMrDQq3pdohAKxiV1Al9g0AVjErqBBuDQLsnDwu1UoNA", + "dtype": "f8" + }, + "yaxis": "y3" + }, + { + "hovertemplate": "Model=Non-Encrypted
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Concurrency (log scale)=%{x}
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After the first token, subsequent tokens stream at this rate. Encrypted is limited by TEE streaming decryption speed; Non-Encrypted is limited by server generation speed at high concurrency." + }, + "width": 1100, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 0.48 + ], + "ticktext": [ + "1", + "2", + "4", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0.5, + 0.98 + ], + "matches": "x", + "ticktext": [ + "1", + "2", + "4", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis3": { + "anchor": "y3", + "domain": [ + 0, + 0.48 + ], + "matches": "x", + "showticklabels": false, + "ticktext": [ + "1", + "2", + "4", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "type": "log" + }, + "xaxis4": { + "anchor": "y4", + "domain": [ + 0.5, + 0.98 + ], + "matches": "x", + "showticklabels": false, + "ticktext": [ + "1", + "2", + "4", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "type": "log" + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 0.485 + ], + "title": { + "text": "Client Output Tok/s p50 (per request)" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0, + 0.485 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis3": { + "anchor": "x3", + "domain": [ + 0.515, + 1 + ], + "matches": "y", + "title": { + "text": "Client Output Tok/s p50 (per request)" + } + }, + "yaxis4": { + "anchor": "x4", + "domain": [ + 0.515, + 1 + ], + "matches": "y", + "showticklabels": false + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Per-request client generation speed\n", + "fig = px.line(\n", + " df_all,\n", + " x='num_concurrent_requests',\n", + " y='client_output_token_per_s_p50',\n", + " color='label',\n", + " symbol='label',\n", + " facet_col='out_tokens_label',\n", + " facet_row='run_type',\n", + " color_discrete_map=COLOR_MAP,\n", + " symbol_map=SYMBOL_MAP,\n", + " log_x=True,\n", + " markers=True,\n", + " labels={\n", + " 'num_concurrent_requests': 'Concurrency (log scale)',\n", + " 'client_output_token_per_s_p50': 'Client Output Tok/s p50 (per request)',\n", + " 'label': 'Model',\n", + " 'out_tokens_label': 'Output Tokens',\n", + " 'run_type': 'Run Type',\n", + " },\n", + " title=(\n", + " 'Client Output Throughput per Request — p50
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Non-Encrypted scales nearly linearly with concurrency. Encrypted collapses at higher concurrencies due to TEE queuing adding large wall-clock delays." + }, + "width": 1100, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 0.48 + ], + "ticktext": [ + "1", + "2", + "4", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0.5, + 0.98 + ], + "matches": "x", + "ticktext": [ + "1", + "2", + "4", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis3": { + "anchor": "y3", + "domain": [ + 0, + 0.48 + ], + "matches": "x", + "showticklabels": false, + "ticktext": [ + "1", + "2", + "4", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "type": "log" + }, + "xaxis4": { + "anchor": "y4", + "domain": [ + 0.5, + 0.98 + ], + "matches": "x", + "showticklabels": false, + "ticktext": [ + "1", + "2", + "4", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 2, + 4, + 8, + 16, + 32 + ], + "type": "log" + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 0.485 + ], + "title": { + "text": "Total Output Throughput (tok/s)" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0, + 0.485 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis3": { + "anchor": "x3", + "domain": [ + 0.515, + 1 + ], + "matches": "y", + "title": { + "text": "Total Output Throughput (tok/s)" + } + }, + "yaxis4": { + "anchor": "x4", + "domain": [ + 0.515, + 1 + ], + "matches": "y", + "showticklabels": false + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Total system throughput — aggregate across all concurrent requests\n", + "fig = px.line(\n", + " df_all,\n", + " x='num_concurrent_requests',\n", + " y='client_total_output_throughput',\n", + " color='label',\n", + " symbol='label',\n", + " facet_col='out_tokens_label',\n", + " facet_row='run_type',\n", + " color_discrete_map=COLOR_MAP,\n", + " symbol_map=SYMBOL_MAP,\n", + " log_x=True,\n", + " markers=True,\n", + " labels={\n", + " 'num_concurrent_requests': 'Concurrency (log scale)',\n", + " 'client_total_output_throughput': 'Total Output Throughput (tok/s)',\n", + " 'label': 'Model',\n", + " 'out_tokens_label': 'Output Tokens',\n", + " 'run_type': 'Run Type',\n", + " },\n", + " title=(\n", + " 'Total Client Output Throughput — All Concurrent Requests
'\n", + " 'Non-Encrypted scales nearly linearly with concurrency. '\n", + " 'Encrypted collapses at higher concurrencies due to TEE queuing adding large wall-clock delays.'\n", + " ),\n", + ")\n", + "fig.update_layout(height=700, width=1100, template='plotly_white')\n", + "fig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cell-dist-hdr", + "metadata": {}, + "source": [ + "## 6. Per-Request Distributions\n", + "\n", + "Box plots using individual request data from all runs.\n", + "- **Multi Run (10x)** provides the richest distributions (10–320 requests per scenario across 10 rounds).\n", + "- **Single Run (1x)** covers only the N simultaneous requests in a single round (1–32 points per box depending on concurrency).\n", + "\n", + "Error requests (where `error_code` is set) are excluded." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "cell-load-individual", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 2,621 individual request records\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " count\n", + "label run_type is_error \n", + "Encrypted Single Run (1x) False 126\n", + " Multi Run (10x) False 1109\n", + "Non-Encrypted Single Run (1x) False 126\n", + " Multi Run (10x) False 1260" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def _parse_fname(fname):\n", + " \"\"\"Extract (out_tokens, concurrency) from benchmark filename.\"\"\"\n", + " base = os.path.basename(str(fname)).replace('_individual_responses.json', '')\n", + " parts = base.split('_')\n", + " try:\n", + " return int(parts[4]), int(parts[5])\n", + " except (IndexError, ValueError):\n", + " return None, None\n", + "\n", + "dfs_ind = []\n", + "for key, meta in DATASETS.items():\n", + " try:\n", + " df_ind = read_perf_eval_json_files(meta['run_dir'], type='individual_responses')\n", + " if df_ind.empty:\n", + " continue\n", + " df_ind['label'] = meta['label']\n", + " df_ind['run_type'] = meta['run_type']\n", + " parsed = df_ind['filename'].apply(lambda f: pd.Series(_parse_fname(f)))\n", + " df_ind['num_output_tokens'] = parsed[0]\n", + " df_ind['num_concurrent_requests'] = parsed[1]\n", + " dfs_ind.append(df_ind)\n", + " except Exception as e:\n", + " print(f'Warning: could not load {key}: {e}')\n", + "\n", + "df_individual = pd.concat(dfs_ind, ignore_index=True)\n", + "df_individual = df_individual.dropna(\n", + " subset=['num_output_tokens', 'num_concurrent_requests', 'client_ttft_s']\n", + ")\n", + "df_individual['num_output_tokens'] = df_individual['num_output_tokens'].astype(int)\n", + "df_individual['num_concurrent_requests'] = df_individual['num_concurrent_requests'].astype(int)\n", + "df_individual['out_tokens_label'] = df_individual['num_output_tokens'].astype(str) + ' output tokens'\n", + "df_individual['concurrency_str'] = df_individual['num_concurrent_requests'].astype(str)\n", + "df_individual['is_error'] = df_individual['error_code'].notna()\n", + "df_individual['run_type'] = pd.Categorical(\n", + " df_individual['run_type'],\n", + " categories=['Single Run (1x)', 'Multi Run (10x)'],\n", + " ordered=True\n", + ")\n", + "\n", + "print(f'Loaded {len(df_individual):,} individual request records')\n", + "df_individual.groupby(['label', 'run_type', 'is_error'])[['client_ttft_s']].count().rename(\n", + " columns={'client_ttft_s': 'count'}\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "cell-box-client-ttft", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "alignmentgroup": "True", + "boxpoints": "outliers", + "hovertemplate": "Model=Encrypted
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Output Tokens=1024 output tokens
Concurrency=%{x}
Client TTFT (s)=%{y}", + "legendgroup": "Non-Encrypted", + "marker": { + "color": "#636EFA" + }, + "name": "Non-Encrypted", + "notched": false, + "offsetgroup": "Non-Encrypted", + "orientation": "v", + "showlegend": true, + "type": "box", + "x": [ + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "8", + "8", + "8", + "8", + "8", + "8", + "8", + "8", + "2", + "2", + "1", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "4", + "4", + "4", + "4" + ], + "x0": " ", + "xaxis": "x3", + "y": { + "bdata": "twphjKuV3T8u5wLniQMEQN2IryJ6/QNAEvaNh5WlA0A0H0SXPO4DQPhS+4cs6wNA8xoyU5faA0B1I64TCwkEQLDIHb4d+wNAX2o6BCF/D0AN5X1GN1YPQFA30pX5Uw9AI57haSm6D0DEM8/uBaEPQIIIlLFpxg9AUDcaIMCOD0D9uUFSvfnOPxfgVMKuJAJAymsok1MsAkC0slRpoQ4CQNYCTJQhGQJALcCRg180AkAwNV0l8SoCQJYlM5z4GgJAIsbAyJOx0j80TEB2GJrSP46p6HOdS8c/XaPmi08i2z990JID4psDQD5BnFlMfwNAaD4osfeRA0CaD9LTa3sDQKBHMx4PlwNANW1WYr2YA0Ajd7Al4Y0DQOFLvf2jmgNA/WPLq3ozD0Bm8K0yD3oPQBCBak5uKA9ATXR07ZkSD0CMKgRTTv0OQM984ERsVQ9A1gIk5vkVD0DmXDX4qgIPQJs2J7JoQRVAwXD1laZrFUBHPEffMmAVQPbdq4T+/hRAio73+39aFUA34kFs3i4VQIq1pEEnwxRALucSsnccFUB90A7HFwAbQE8QuYRtSxtANEYB0usMG0BoZfHy8c8aQBa5L6Iw0hpAp83qjvQzG0D0aMzFHe0aQHKH2RmcA9U/9Y9tEhX91D+2ACeMrNjUPxkNU3W/lfU/", + "dtype": "f8" + }, + "y0": " ", + "yaxis": "y3" + }, + { + "alignmentgroup": "True", + "boxpoints": "outliers", + "hovertemplate": "Model=Non-Encrypted
Run Type=Single Run (1x)
Output Tokens=100 output tokens
Concurrency=%{x}
Client TTFT (s)=%{y}", + "legendgroup": "Non-Encrypted", + "marker": { + "color": "#636EFA" + }, + "name": "Non-Encrypted", + "notched": false, + "offsetgroup": "Non-Encrypted", + "orientation": "v", + "showlegend": false, + "type": "box", + "x": [ + "2", + "2", + "4", + "4", + "4", + "4", + "1", + "8", + "8", + "8", + "8", + "8", + "8", + "8", + "8", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32" + ], + "x0": " ", + "xaxis": "x4", + "y": { + "bdata": "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", + "dtype": "f8" + }, + "y0": " ", + "yaxis": "y4" + }, + { + "alignmentgroup": "True", + "boxpoints": "outliers", + "hovertemplate": "Model=Encrypted
Run Type=Multi Run (10x)
Output Tokens=1024 output tokens
Concurrency=%{x}
Client TTFT (s)=%{y}", + "legendgroup": "Encrypted", + "marker": { + "color": "#EF553B" + }, + "name": "Encrypted", + "notched": false, + "offsetgroup": "Encrypted", + "orientation": "v", + "showlegend": false, + "type": "box", + "x": [ + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + 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Run Type=Multi Run (10x)
Output Tokens=100 output tokens
Concurrency=%{x}
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Run Type=Multi Run (10x)
Output Tokens=1024 output tokens
Concurrency=%{x}
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Run Type=Multi Run (10x)
Output Tokens=100 output tokens
Concurrency=%{x}
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'\n", + " 'Encrypted shows higher median AND wider spread due to variable TEE network latency. '\n", + " 'High concurrency increases spread (TEE queue wait time variance).'\n", + " ),\n", + ")\n", + "fig.update_layout(height=700, width=1100, template='plotly_white')\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "cell-box-server-ttft", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "alignmentgroup": "True", + "boxpoints": "outliers", + "hovertemplate": "Model=Encrypted
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Server TTFT (s)=%{y}", + "legendgroup": "Non-Encrypted", + "marker": { + "color": "#636EFA" + }, + "name": "Non-Encrypted", + "notched": false, + "offsetgroup": "Non-Encrypted", + "orientation": "v", + "showlegend": false, + "type": "box", + "x": [ + "2", + "2", + "4", + "4", + "4", + "4", + "1", + "8", + "8", + "8", + "8", + "8", + "8", + "8", + "8", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32" + ], + "x0": " ", + "xaxis": "x4", + "y": { + "bdata": "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", + "dtype": "f8" + }, + "y0": " ", + "yaxis": "y4" + }, + { + "alignmentgroup": "True", + "boxpoints": "outliers", + "hovertemplate": "Model=Encrypted
Run Type=Multi Run (10x)
Output Tokens=1024 output tokens
Concurrency=%{x}
Server TTFT (s)=%{y}", + "legendgroup": "Encrypted", + "marker": { + "color": "#EF553B" + }, + "name": "Encrypted", + "notched": false, + "offsetgroup": "Encrypted", + "orientation": "v", + "showlegend": false, + "type": "box", + "x": [ + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + 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+ "dtype": "f8" + }, + "y0": " ", + "yaxis": "y" + }, + { + "alignmentgroup": "True", + "boxpoints": "outliers", + "hovertemplate": "Model=Encrypted
Run Type=Multi Run (10x)
Output Tokens=100 output tokens
Concurrency=%{x}
Server TTFT (s)=%{y}", + "legendgroup": "Encrypted", + "marker": { + "color": "#EF553B" + }, + "name": "Encrypted", + "notched": false, + "offsetgroup": "Encrypted", + "orientation": "v", + "showlegend": false, + "type": "box", + "x": [ + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + 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Run Type=Multi Run (10x)
Output Tokens=100 output tokens
Concurrency=%{x}
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'\n", + " 'Server-side distribution. At medium concurrencies, encrypted may show LOWER server TTFT '\n", + " 'because TEE de-batching reduces effective batch size seen by the server.'\n", + " ),\n", + ")\n", + "fig.update_layout(height=700, width=1100, template='plotly_white')\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "cell-scatter-tee", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Output Tokens=100 output tokens
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Shows how network latency varies independently from server TTFT. A flat horizontal spread would indicate network latency is independent of server load." + }, + "width": 1400, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 0.15 + ], + "title": { + "text": "Server TTFT (s)" + } + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0.16999999999999998, + 0.31999999999999995 + ], + "matches": "x", + "title": { + "text": "Server TTFT (s)" + } + }, + "xaxis3": { + "anchor": "y3", + "domain": [ + 0.33999999999999997, + 0.49 + ], + "matches": "x", + "title": { + "text": "Server TTFT (s)" + } + }, + "xaxis4": { + "anchor": "y4", + "domain": [ + 0.51, + 0.66 + ], + "matches": "x", + "title": { + "text": "Server TTFT (s)" + } + }, + "xaxis5": { + "anchor": "y5", + "domain": [ + 0.6799999999999999, + 0.83 + ], + "matches": "x", + "title": { + "text": "Server TTFT (s)" + } + }, + "xaxis6": { + "anchor": "y6", + "domain": [ + 0.85, + 1 + ], + "matches": "x", + "title": { + "text": "Server TTFT (s)" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "TEE Network Latency (ms)" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis3": { + "anchor": "x3", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis4": { + "anchor": "x4", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis5": { + "anchor": "x5", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis6": { + "anchor": "x6", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Per-request: TEE network latency vs server TTFT (encrypted multi run only)\n", + "df_enc_ind = df_individual[\n", + " (df_individual['label'] == 'Encrypted') &\n", + " (df_individual['run_type'] == 'Multi Run (10x)') &\n", + " (~df_individual['is_error']) &\n", + " (df_individual['server_network_latency_ms'].notna())\n", + "].copy()\n", + "\n", + "fig = px.scatter(\n", + " df_enc_ind,\n", + " x='server_ttft_s',\n", + " y='server_network_latency_ms',\n", + " color='out_tokens_label',\n", + " facet_col='concurrency_str',\n", + " opacity=0.55,\n", + " category_orders={'concurrency_str': CONC_STR},\n", + " labels={\n", + " 'server_ttft_s': 'Server TTFT (s)',\n", + " 'server_network_latency_ms': 'TEE Network Latency (ms)',\n", + " 'out_tokens_label': 'Output Tokens',\n", + " 'concurrency_str': 'Concurrency',\n", + " },\n", + " title=(\n", + " 'Per-Request: TEE Network Latency vs Server TTFT — Encrypted Multi Run
'\n", + " 'Shows how network latency varies independently from server TTFT. '\n", + " 'A flat horizontal spread would indicate network latency is independent of server load.'\n", + " ),\n", + ")\n", + "fig.update_layout(height=420, width=1400, template='plotly_white')\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cell-summary-hdr", + "metadata": {}, + "source": [ + "## 7. 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 MetricRun TypeOutput TokensConcurrencyNon-EncryptedEncryptedDelta% Change
108Client E2E p50 (s)Multi Run (10x)10010.3770.504+0.127+33.7%
109Client E2E p50 (s)Multi Run (10x)10020.6340.658+0.024+3.8%
110Client E2E p50 (s)Multi Run (10x)10040.6580.685+0.027+4.1%
111Client E2E p50 (s)Multi Run (10x)10080.8530.838-0.015-1.7%
112Client E2E p50 (s)Multi Run (10x)100161.0730.791-0.282-26.3%
113Client E2E p50 (s)Multi Run (10x)100322.1650.918-1.247-57.6%
114Client E2E p50 (s)Multi Run (10x)102411.8312.038+0.206+11.3%
115Client E2E p50 (s)Multi Run (10x)102423.6062.692-0.914-25.3%
116Client E2E p50 (s)Multi Run (10x)102442.8863.243+0.357+12.4%
117Client E2E p50 (s)Multi Run (10x)102483.2743.982+0.709+21.6%
118Client E2E p50 (s)Multi Run (10x)1024162.9634.137+1.174+39.6%
119Client E2E p50 (s)Multi Run (10x)1024325.9574.933-1.024-17.2%
96Client E2E p50 (s)Single Run (1x)10010.6121.169+0.558+91.2%
97Client E2E p50 (s)Single Run (1x)10020.5360.932+0.396+73.8%
98Client E2E p50 (s)Single Run (1x)10040.8600.843-0.017-2.0%
99Client E2E p50 (s)Single Run (1x)10081.0151.088+0.073+7.1%
100Client E2E p50 (s)Single Run (1x)100161.1171.173+0.056+5.0%
101Client E2E p50 (s)Single Run (1x)100322.2631.918-0.345-15.2%
102Client E2E p50 (s)Single Run (1x)102411.8290.355-1.474-80.6%
103Client E2E p50 (s)Single Run (1x)102422.1241.987-0.137-6.5%
104Client E2E p50 (s)Single Run (1x)102441.2042.556+1.352+112.3%
105Client E2E p50 (s)Single Run (1x)102483.3793.462+0.083+2.5%
106Client E2E p50 (s)Single Run (1x)1024163.6184.532+0.914+25.2%
107Client E2E p50 (s)Single Run (1x)1024325.0436.671+1.628+32.3%
84Client TTFT p50 (s)Multi Run (10x)10010.2180.335+0.117+53.5%
85Client TTFT p50 (s)Multi Run (10x)10020.4740.492+0.018+3.7%
86Client TTFT p50 (s)Multi Run (10x)10040.5270.559+0.032+6.0%
87Client TTFT p50 (s)Multi Run (10x)10080.6890.699+0.011+1.6%
88Client TTFT p50 (s)Multi Run (10x)100160.9100.672-0.238-26.1%
89Client TTFT p50 (s)Multi Run (10x)100322.0010.799-1.202-60.1%
90Client TTFT p50 (s)Multi Run (10x)102410.1830.326+0.143+78.3%
91Client TTFT p50 (s)Multi Run (10x)102421.9601.729-0.230-11.7%
92Client TTFT p50 (s)Multi Run (10x)102442.0062.103+0.097+4.8%
93Client TTFT p50 (s)Multi Run (10x)102482.1542.674+0.519+24.1%
94Client TTFT p50 (s)Multi Run (10x)1024161.8472.697+0.851+46.1%
95Client TTFT p50 (s)Multi Run (10x)1024324.8384.000-0.838-17.3%
72Client TTFT p50 (s)Single Run (1x)10010.4511.169+0.718+159.1%
73Client TTFT p50 (s)Single Run (1x)10020.3770.765+0.388+102.8%
74Client TTFT p50 (s)Single Run (1x)10040.7330.780+0.047+6.5%
75Client TTFT p50 (s)Single Run (1x)10080.8540.959+0.104+12.2%
76Client TTFT p50 (s)Single Run (1x)100160.9571.016+0.059+6.1%
77Client TTFT p50 (s)Single Run (1x)100322.0981.752-0.346-16.5%
78Client TTFT p50 (s)Single Run (1x)102410.1820.355+0.173+95.0%
79Client TTFT p50 (s)Single Run (1x)102420.2911.163+0.872+299.1%
80Client TTFT p50 (s)Single Run (1x)102440.3280.683+0.355+108.2%
81Client TTFT p50 (s)Single Run (1x)102482.2652.730+0.464+20.5%
82Client TTFT p50 (s)Single Run (1x)1024162.5032.833+0.330+13.2%
83Client TTFT p50 (s)Single Run (1x)1024323.9265.016+1.091+27.8%
132Client Tok/s p50Multi Run (10x)1001629.685590.152-39.533-6.3%
133Client Tok/s p50Multi Run (10x)1002628.471592.733-35.738-5.7%
134Client Tok/s p50Multi Run (10x)1004777.635593.165-184.469-23.7%
135Client Tok/s p50Multi Run (10x)1008612.022593.329-18.693-3.1%
136Client Tok/s p50Multi Run (10x)10016612.372594.182-18.190-3.0%
137Client Tok/s p50Multi Run (10x)10032611.674608.532-3.142-0.5%
138Client Tok/s p50Multi Run (10x)10241621.394584.694-36.700-5.9%
139Client Tok/s p50Multi Run (10x)10242621.498585.532-35.966-5.8%
140Client Tok/s p50Multi Run (10x)10244785.076584.811-200.265-25.5%
141Client Tok/s p50Multi Run (10x)10248616.396588.443-27.954-4.5%
142Client Tok/s p50Multi Run (10x)102416616.397588.818-27.579-4.5%
143Client Tok/s p50Multi Run (10x)102432615.732591.864-23.869-3.9%
120Client Tok/s p50Single Run (1x)1001624.004———
121Client Tok/s p50Single Run (1x)1002628.968597.872-31.096-4.9%
122Client Tok/s p50Single Run (1x)1004782.864786.389+3.524+0.5%
123Client Tok/s p50Single Run (1x)1008623.630594.143-29.487-4.7%
124Client Tok/s p50Single Run (1x)10016611.377622.960+11.583+1.9%
125Client Tok/s p50Single Run (1x)10032618.338604.707-13.631-2.2%
126Client Tok/s p50Single Run (1x)10241621.915———
127Client Tok/s p50Single Run (1x)10242558.812551.040-7.772-1.4%
128Client Tok/s p50Single Run (1x)10244784.519528.549-255.971-32.6%
129Client Tok/s p50Single Run (1x)10248616.223583.179-33.044-5.4%
130Client Tok/s p50Single Run (1x)102416615.858594.350-21.508-3.5%
131Client Tok/s p50Single Run (1x)102432616.906593.955-22.952-3.7%
204Error Rate (%)Multi Run (10x)10010.0000.000+0.000—
205Error Rate (%)Multi Run (10x)10020.0000.000+0.000—
206Error Rate (%)Multi Run (10x)10040.0000.000+0.000—
207Error Rate (%)Multi Run (10x)10080.0000.000+0.000—
208Error Rate (%)Multi Run (10x)100160.00011.250+11.250—
209Error Rate (%)Multi Run (10x)100320.00019.375+19.375—
210Error Rate (%)Multi Run (10x)102410.0000.000+0.000—
211Error Rate (%)Multi Run (10x)102420.0000.000+0.000—
212Error Rate (%)Multi Run (10x)102440.0000.000+0.000—
213Error Rate (%)Multi Run (10x)102480.0001.250+1.250—
214Error Rate (%)Multi Run (10x)1024160.0008.750+8.750—
215Error Rate (%)Multi Run (10x)1024320.00017.500+17.500—
192Error Rate (%)Single Run (1x)10010.0000.000+0.000—
193Error Rate (%)Single Run (1x)10020.0000.000+0.000—
194Error Rate (%)Single Run (1x)10040.0000.000+0.000—
195Error Rate (%)Single Run (1x)10080.0000.000+0.000—
196Error Rate (%)Single Run (1x)100160.0000.000+0.000—
197Error Rate (%)Single Run (1x)100320.0000.000+0.000—
198Error Rate (%)Single Run (1x)102410.0000.000+0.000—
199Error Rate (%)Single Run (1x)102420.0000.000+0.000—
200Error Rate (%)Single Run (1x)102440.0000.000+0.000—
201Error Rate (%)Single Run (1x)102480.0000.000+0.000—
202Error Rate (%)Single Run (1x)1024160.0000.000+0.000—
203Error Rate (%)Single Run (1x)1024320.0000.000+0.000—
180Req / minMulti Run (10x)1001153.054109.881-43.173-28.2%
181Req / minMulti Run (10x)1002186.242179.823-6.419-3.4%
182Req / minMulti Run (10x)1004357.74431.636-326.108-91.2%
183Req / minMulti Run (10x)1008549.81736.342-513.474-93.4%
184Req / minMulti Run (10x)10016839.31329.502-809.811-96.5%
185Req / minMulti Run (10x)10032848.29928.436-819.864-96.6%
186Req / minMulti Run (10x)1024132.57434.113+1.539+4.7%
187Req / minMulti Run (10x)1024237.78144.928+7.146+18.9%
188Req / minMulti Run (10x)1024482.55546.973-35.583-43.1%
189Req / minMulti Run (10x)10248144.65730.085-114.572-79.2%
190Req / minMulti Run (10x)102416299.23728.748-270.489-90.4%
191Req / minMulti Run (10x)102432309.42330.504-278.919-90.1%
168Req / minSingle Run (1x)100197.33051.228-46.102-47.4%
169Req / minSingle Run (1x)1002171.519109.210-62.310-36.3%
170Req / minSingle Run (1x)1004275.731262.001-13.730-5.0%
171Req / minSingle Run (1x)1008463.535423.746-39.789-8.6%
172Req / minSingle Run (1x)10016570.178546.859-23.319-4.1%
173Req / minSingle Run (1x)10032596.145627.558+31.413+5.3%
174Req / minSingle Run (1x)1024132.659168.327+135.668+415.4%
175Req / minSingle Run (1x)1024256.10555.153-0.952-1.7%
176Req / minSingle Run (1x)1024479.89652.826-27.070-33.9%
177Req / minSingle Run (1x)10248140.031110.432-29.599-21.1%
178Req / minSingle Run (1x)102416186.128147.481-38.647-20.8%
179Req / minSingle Run (1x)102432237.265175.776-61.489-25.9%
60Server E2E p50 (s)Multi Run (10x)10010.2510.260+0.010+3.8%
61Server E2E p50 (s)Multi Run (10x)10020.2510.259+0.008+3.2%
62Server E2E p50 (s)Multi Run (10x)10040.2880.262-0.027-9.2%
63Server E2E p50 (s)Multi Run (10x)10080.4740.291-0.183-38.6%
64Server E2E p50 (s)Multi Run (10x)100160.4740.294-0.181-38.1%
65Server E2E p50 (s)Multi Run (10x)100320.4750.293-0.182-38.4%
66Server E2E p50 (s)Multi Run (10x)102411.7401.807+0.067+3.9%
67Server E2E p50 (s)Multi Run (10x)102421.7401.785+0.046+2.6%
68Server E2E p50 (s)Multi Run (10x)102441.0371.550+0.513+49.5%
69Server E2E p50 (s)Multi Run (10x)102481.4291.490+0.062+4.3%
70Server E2E p50 (s)Multi Run (10x)1024161.4291.751+0.322+22.5%
71Server E2E p50 (s)Multi Run (10x)1024321.4281.622+0.194+13.6%
48Server E2E p50 (s)Single Run (1x)10010.2510.094-0.158-62.6%
49Server E2E p50 (s)Single Run (1x)10020.2510.261+0.011+4.3%
50Server E2E p50 (s)Single Run (1x)10040.2900.276-0.014-4.9%
51Server E2E p50 (s)Single Run (1x)10080.4780.446-0.032-6.8%
52Server E2E p50 (s)Single Run (1x)100160.4760.398-0.079-16.5%
53Server E2E p50 (s)Single Run (1x)100320.4770.481+0.003+0.6%
54Server E2E p50 (s)Single Run (1x)102411.7380.093-1.646-94.7%
55Server E2E p50 (s)Single Run (1x)102422.0010.916-1.085-54.2%
56Server E2E p50 (s)Single Run (1x)102441.0361.913+0.877+84.7%
57Server E2E p50 (s)Single Run (1x)102481.4271.752+0.325+22.8%
58Server E2E p50 (s)Single Run (1x)1024161.4321.882+0.450+31.4%
59Server E2E p50 (s)Single Run (1x)1024321.4281.769+0.341+23.9%
12Server TTFT p50 (s)Multi Run (10x)10010.0910.091-0.000-0.1%
13Server TTFT p50 (s)Multi Run (10x)10020.0910.090-0.001-1.1%
14Server TTFT p50 (s)Multi Run (10x)10040.1610.162+0.001+0.4%
15Server TTFT p50 (s)Multi Run (10x)10080.3110.162-0.149-47.9%
16Server TTFT p50 (s)Multi Run (10x)100160.3110.165-0.146-47.1%
17Server TTFT p50 (s)Multi Run (10x)100320.3120.164-0.148-47.5%
18Server TTFT p50 (s)Multi Run (10x)102410.0920.091-0.001-0.5%
19Server TTFT p50 (s)Multi Run (10x)102420.0920.092-0.000-0.4%
20Server TTFT p50 (s)Multi Run (10x)102440.1610.163+0.003+1.6%
21Server TTFT p50 (s)Multi Run (10x)102480.3120.161-0.151-48.5%
22Server TTFT p50 (s)Multi Run (10x)1024160.3110.311-0.001-0.2%
23Server TTFT p50 (s)Multi Run (10x)1024320.3110.167-0.144-46.4%
0Server TTFT p50 (s)Single Run (1x)10010.0910.093+0.001+1.6%
1Server TTFT p50 (s)Single Run (1x)10020.0910.092+0.001+1.0%
2Server TTFT p50 (s)Single Run (1x)10040.1630.162-0.001-0.4%
3Server TTFT p50 (s)Single Run (1x)10080.3160.313-0.003-1.0%
4Server TTFT p50 (s)Single Run (1x)100160.3140.314-0.000-0.1%
5Server TTFT p50 (s)Single Run (1x)100320.3140.314-0.000-0.1%
6Server TTFT p50 (s)Single Run (1x)102410.0910.092+0.001+0.8%
7Server TTFT p50 (s)Single Run (1x)102420.1680.091-0.077-45.8%
8Server TTFT p50 (s)Single Run (1x)102440.1600.131-0.029-18.0%
9Server TTFT p50 (s)Single Run (1x)102480.3110.314+0.003+1.0%
10Server TTFT p50 (s)Single Run (1x)1024160.3130.314+0.001+0.2%
11Server TTFT p50 (s)Single Run (1x)1024320.3110.314+0.004+1.1%
36Server Tok/s p50Multi Run (10x)1001619.416584.803-34.614-5.6%
37Server Tok/s p50Multi Run (10x)1002619.315584.746-34.568-5.6%
38Server Tok/s p50Multi Run (10x)1004776.275585.039-191.236-24.6%
39Server Tok/s p50Multi Run (10x)1008607.891584.452-23.439-3.9%
40Server Tok/s p50Multi Run (10x)10016605.648584.301-21.348-3.5%
41Server Tok/s p50Multi Run (10x)10032605.877593.517-12.360-2.0%
42Server Tok/s p50Multi Run (10x)10241620.923585.042-35.880-5.8%
43Server Tok/s p50Multi Run (10x)10242620.731585.130-35.601-5.7%
44Server Tok/s p50Multi Run (10x)10244784.177538.217-245.960-31.4%
45Server Tok/s p50Multi Run (10x)10248615.332585.288-30.045-4.9%
46Server Tok/s p50Multi Run (10x)102416614.865587.584-27.281-4.4%
47Server Tok/s p50Multi Run (10x)102432614.868589.279-25.588-4.2%
24Server Tok/s p50Single Run (1x)1001618.574———
25Server Tok/s p50Single Run (1x)1002619.233583.614-35.619-5.8%
26Server Tok/s p50Single Run (1x)1004776.964765.256-11.709-1.5%
27Server Tok/s p50Single Run (1x)1008609.028588.329-20.699-3.4%
28Server Tok/s p50Single Run (1x)10016608.304595.598-12.706-2.1%
29Server Tok/s p50Single Run (1x)10032605.646594.129-11.517-1.9%
30Server Tok/s p50Single Run (1x)10241620.919———
31Server Tok/s p50Single Run (1x)10242558.313545.003-13.310-2.4%
32Server Tok/s p50Single Run (1x)10244783.937527.983-255.954-32.6%
33Server Tok/s p50Single Run (1x)10248615.830581.324-34.506-5.6%
34Server Tok/s p50Single Run (1x)102416615.436593.467-21.970-3.6%
35Server Tok/s p50Single Run (1x)102432615.421593.045-22.376-3.6%
156Total Throughput (tok/s)Multi Run (10x)1001255.090183.135-71.955-28.2%
157Total Throughput (tok/s)Multi Run (10x)1002310.403246.657-63.746-20.5%
158Total Throughput (tok/s)Multi Run (10x)1004596.24037.515-558.725-93.7%
159Total Throughput (tok/s)Multi Run (10x)1008916.36150.652-865.709-94.5%
160Total Throughput (tok/s)Multi Run (10x)100161398.85542.681-1356.174-96.9%
161Total Throughput (tok/s)Multi Run (10x)100321413.83236.474-1377.359-97.4%
162Total Throughput (tok/s)Multi Run (10x)10241555.924470.868-85.056-15.3%
163Total Throughput (tok/s)Multi Run (10x)10242644.798536.361-108.437-16.8%
164Total Throughput (tok/s)Multi Run (10x)102441062.210618.492-443.718-41.8%
165Total Throughput (tok/s)Multi Run (10x)102481759.995366.618-1393.377-79.2%
166Total Throughput (tok/s)Multi Run (10x)1024163441.721324.169-3117.552-90.6%
167Total Throughput (tok/s)Multi Run (10x)1024323553.464356.617-3196.847-90.0%
144Total Throughput (tok/s)Single Run (1x)1001162.2160.000-162.216-100.0%
145Total Throughput (tok/s)Single Run (1x)1002285.866182.016-103.850-36.3%
146Total Throughput (tok/s)Single Run (1x)1004459.552327.501-132.051-28.7%
147Total Throughput (tok/s)Single Run (1x)1008772.559456.410-316.149-40.9%
148Total Throughput (tok/s)Single Run (1x)10016950.296626.609-323.687-34.1%
149Total Throughput (tok/s)Single Run (1x)10032993.576891.982-101.593-10.2%
150Total Throughput (tok/s)Single Run (1x)10241557.3810.000-557.381-100.0%
151Total Throughput (tok/s)Single Run (1x)10242957.525441.227-516.298-53.9%
152Total Throughput (tok/s)Single Run (1x)102441027.992666.924-361.068-35.1%
153Total Throughput (tok/s)Single Run (1x)102481703.7101250.645-453.065-26.6%
154Total Throughput (tok/s)Single Run (1x)1024162199.4101782.985-416.425-18.9%
155Total Throughput (tok/s)Single Run (1x)1024322762.1562164.786-597.370-21.6%
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DIMS = ['run_type', 'num_output_tokens', 'num_concurrent_requests']\n", + "\n", + "ALL_METRICS = [\n", + " ('server_ttft_s_p50', 'Server TTFT p50 (s)'),\n", + " ('server_output_token_per_s_p50', 'Server Tok/s p50'),\n", + " ('server_end_to_end_latency_s_p50', 'Server E2E p50 (s)'),\n", + " ('client_ttft_s_p50', 'Client TTFT p50 (s)'),\n", + " ('client_end_to_end_latency_s_p50', 'Client E2E p50 (s)'),\n", + " ('client_output_token_per_s_p50', 'Client Tok/s p50'),\n", + " ('client_total_output_throughput', 'Total Throughput (tok/s)'),\n", + " ('num_completed_requests_per_min', 'Req / min'),\n", + " ('error_rate_pct', 'Error Rate (%)'),\n", + "]\n", + "\n", + "df_enc_s = df_all[df_all['label'] == 'Encrypted'].set_index(DIMS)\n", + "df_plain_s = df_all[df_all['label'] == 'Non-Encrypted'].set_index(DIMS)\n", + "\n", + "rows = []\n", + "for col, label in ALL_METRICS:\n", + " if col not in df_enc_s.columns:\n", + " continue\n", + " for idx in df_enc_s.index:\n", + " if idx not in df_plain_s.index:\n", + " continue\n", + " enc_v = df_enc_s.loc[idx, col]\n", + " plain_v = df_plain_s.loc[idx, col]\n", + " if pd.isna(enc_v) and pd.isna(plain_v):\n", + " continue\n", + " delta = enc_v - plain_v if pd.notna(enc_v) and pd.notna(plain_v) else np.nan\n", + " pct_chg = delta / plain_v * 100 if pd.notna(delta) and plain_v != 0 else np.nan\n", + " rows.append({\n", + " 'Metric': label,\n", + " 'Run Type': idx[0],\n", + " 'Output Tokens': idx[1],\n", + " 'Concurrency': idx[2],\n", + " 'Non-Encrypted': plain_v,\n", + " 'Encrypted': enc_v,\n", + " 'Delta': delta,\n", + " '% Change': pct_chg,\n", + " })\n", + "\n", + "df_summary = pd.DataFrame(rows).sort_values(\n", + " ['Metric', 'Run Type', 'Output Tokens', 'Concurrency']\n", + ")\n", + "\n", + "def _pct_color(val):\n", + " if pd.isna(val): return ''\n", + " if val > 100: return 'background-color: #ff6666'\n", + " if val > 50: return 'background-color: #ffaaaa'\n", + " if val > 20: return 'background-color: #ffd8aa'\n", + " if val > 0: return 'background-color: #fffacc'\n", + " if val < -20: return 'background-color: #aaffaa'\n", + " return ''\n", + "\n", + "df_summary.style \\\n", + " .applymap(_pct_color, subset=['% Change']) \\\n", + " .format({\n", + " 'Non-Encrypted': '{:.3f}',\n", + " 'Encrypted': '{:.3f}',\n", + " 'Delta': '{:+.3f}',\n", + " '% Change': '{:+.1f}%',\n", + " }, na_rep='—')" + ] + }, + { + "cell_type": "markdown", + "id": "cell-tee-overhead-hdr", + "metadata": {}, + "source": [ + "### TEE Overhead Attribution per Scenario" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "cell-tee-overhead-table", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", 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 Run TypeOutput TokensConcurrencyPlain TTFT (s)Enc TTFT (s)Delta (s)Encryption (s)Decryption (s)Network Hop (s)TEE Total (s)TEE % of Enc TTFTNetwork %Encrypt %Decrypt %
0Single Run (1x)10010.45131.16920.71790.00460.00011.06021.064991.07955090.6736490.3975370.008365
1Single Run (1x)10020.37720.76490.38770.00540.00080.82310.8294108.430658107.6111390.7121060.107413
2Single Run (1x)10040.73260.78000.04740.00430.00050.67260.677386.83739786.2309360.5458210.060641
3Single Run (1x)10080.85430.95870.10440.00390.00040.88670.891092.93817792.4866170.4083650.043194
4Single Run (1x)100160.95711.01570.05860.00390.00031.06181.0660104.955459104.5339080.3874770.034075
5Single Run (1x)100322.09841.7525-0.34590.00390.00041.85711.8614106.211863105.9705850.2212610.020017
6Single Run (1x)102410.18200.35490.17290.00490.00010.25170.256772.32637470.9268811.3764440.023049
7Single Run (1x)102420.29141.16300.87160.00450.00341.86821.8761161.314832160.6382720.3857950.290765
8Single Run (1x)102440.32810.68310.35500.00430.00432.39572.4043351.971644350.7104960.6259550.635193
9Single Run (1x)102482.26552.72990.46440.00420.00313.23193.2392118.655980118.3893660.1527930.113821
10Single Run (1x)1024162.50312.83290.32980.00400.00304.41894.4258156.230086155.9859760.1399380.104172
11Single Run (1x)1024323.92575.01631.09060.00390.00296.59986.6067131.704180131.5679330.0782290.058019
12Multi Run (10x)10010.21820.33490.11670.00980.00370.42470.4382130.842490126.8127202.9180651.111705
13Multi Run (10x)10020.47430.49180.01750.00880.00190.53660.5473111.287556109.1113871.7882680.387902
14Multi Run (10x)10040.52750.55930.03180.00790.00110.59950.6085108.803576107.1884141.4143570.200805
15Multi Run (10x)10080.68860.69930.01070.00700.00110.63840.646592.44770591.2911341.0039610.152610
16Multi Run (10x)100160.91040.6725-0.23790.00520.00080.75610.7621113.326543112.4370410.7755090.113993
17Multi Run (10x)100322.00090.7989-1.20200.00550.00070.80320.8094101.318313100.5438600.6860060.088447
18Multi Run (10x)102410.18310.32640.14330.00910.01091.60791.6279498.737439492.6164222.7876843.333333
19Multi Run (10x)102421.95951.7294-0.23010.00910.00762.43222.4490141.608199140.6401120.5287090.439378
20Multi Run (10x)102442.00602.10310.09710.00940.00712.73462.7512130.815658130.0292380.4481190.338301
21Multi Run (10x)102482.15432.67370.51940.00890.00642.26622.281485.32840384.7570970.3311850.240120
22Multi Run (10x)1024161.84672.69730.85060.00810.00522.67912.692499.81681799.3262110.2994110.191195
23Multi Run (10x)1024324.83814.0002-0.83790.00590.00523.14913.160279.00075778.7228040.1483080.129646
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DIMS = ['run_type', 'num_output_tokens', 'num_concurrent_requests']\n", + "\n", + "df_tee_oh = df_all[df_all['label'] == 'Encrypted'].copy()\n", + "df_tee_oh = df_tee_oh.merge(\n", + " df_all[df_all['label'] == 'Non-Encrypted'][DIMS + ['client_ttft_s_p50']].rename(\n", + " columns={'client_ttft_s_p50': 'client_ttft_plain'}\n", + " ),\n", + " on=DIMS,\n", + ")\n", + "\n", + "df_tee_oh['enc_time_s'] = df_tee_oh['total_encryption_time_ms_mean'] / 1000\n", + "df_tee_oh['dec_time_s'] = df_tee_oh['total_decryption_time_ms_mean'] / 1000\n", + "df_tee_oh['network_s'] = df_tee_oh['server_network_latency_ms_mean'] / 1000\n", + "df_tee_oh['tee_total_s'] = df_tee_oh['enc_time_s'] + df_tee_oh['dec_time_s'] + df_tee_oh['network_s']\n", + "df_tee_oh['ttft_delta_s'] = df_tee_oh['client_ttft_s_p50'] - df_tee_oh['client_ttft_plain']\n", + "df_tee_oh['network_pct'] = df_tee_oh['network_s'] / df_tee_oh['client_ttft_s_p50'] * 100\n", + "df_tee_oh['enc_pct'] = df_tee_oh['enc_time_s'] / df_tee_oh['client_ttft_s_p50'] * 100\n", + "df_tee_oh['dec_pct'] = df_tee_oh['dec_time_s'] / df_tee_oh['client_ttft_s_p50'] * 100\n", + "df_tee_oh['tee_total_pct'] = df_tee_oh['tee_total_s'] / df_tee_oh['client_ttft_s_p50'] * 100\n", + "\n", + "DISPLAY = {\n", + " 'run_type': 'Run Type',\n", + " 'num_output_tokens': 'Output Tokens',\n", + " 'num_concurrent_requests': 'Concurrency',\n", + " 'client_ttft_plain': 'Plain TTFT (s)',\n", + " 'client_ttft_s_p50': 'Enc TTFT (s)',\n", + " 'ttft_delta_s': 'Delta (s)',\n", + " 'enc_time_s': 'Encryption (s)',\n", + " 'dec_time_s': 'Decryption (s)',\n", + " 'network_s': 'Network Hop (s)',\n", + " 'tee_total_s': 'TEE Total (s)',\n", + " 'tee_total_pct': 'TEE % of Enc TTFT',\n", + " 'network_pct': 'Network %',\n", + " 'enc_pct': 'Encrypt %',\n", + " 'dec_pct': 'Decrypt %',\n", + "}\n", + "\n", + "df_tee_display = (\n", + " df_tee_oh[list(DISPLAY.keys())]\n", + " .rename(columns=DISPLAY)\n", + " .sort_values(['Run Type', 'Output Tokens', 'Concurrency'])\n", + ")\n", + "\n", + "pct_cols = [c for c in df_tee_display.columns if '%' in c]\n", + "s_cols = [c for c in df_tee_display.columns if '(s)' in c]\n", + "\n", + "df_tee_display.style \\\n", + " .background_gradient(subset=['TEE % of Enc TTFT'], cmap='YlOrRd') \\\n", + " .format({c: '{:.1f}%' for c in pct_cols}) \\\n", + " .format({c: '{:.4f}' for c in s_cols}, na_rep='—')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56a895dd", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7cf9ba4", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From ec9a0d37d6bee5ba6eebd42bcee336ef2b54e818 Mon Sep 17 00:00:00 2001 From: Rodrigo Maldonado Date: Thu, 7 May 2026 17:26:24 -0500 Subject: [PATCH 14/17] model configs for complete runs --- ...akrypto-complete-encrypted-model_configs.csv | 17 +++++++++++++++++ ...pto-complete-non_encrypted-model_configs.csv | 17 +++++++++++++++++ 2 files changed, 34 insertions(+) create mode 100644 benchmarking/benchmarking_bundles/datakrypto-complete-encrypted-model_configs.csv create mode 100644 benchmarking/benchmarking_bundles/datakrypto-complete-non_encrypted-model_configs.csv diff --git a/benchmarking/benchmarking_bundles/datakrypto-complete-encrypted-model_configs.csv b/benchmarking/benchmarking_bundles/datakrypto-complete-encrypted-model_configs.csv new file mode 100644 index 000000000..10befcddf --- /dev/null +++ b/benchmarking/benchmarking_bundles/datakrypto-complete-encrypted-model_configs.csv @@ -0,0 +1,17 @@ +model_name,input_tokens,output_tokens,num_requests,concurrent_requests,qps,qps_distribution,multimodal_img_size +Llama-xLAM-2-8b-fc-r-encrypted,3900,100,10,1,,, +Llama-xLAM-2-8b-fc-r-encrypted,3900,100,30,3,,, +Llama-xLAM-2-8b-fc-r-encrypted,3900,100,50,5,,, +Llama-xLAM-2-8b-fc-r-encrypted,3900,100,80,8,,, +Llama-xLAM-2-8b-fc-r-encrypted,3900,100,160,16,,, +Llama-xLAM-2-8b-fc-r-encrypted,3900,100,320,32,,, +Llama-xLAM-2-8b-fc-r-encrypted,8000,100,10,1,,, +Llama-xLAM-2-8b-fc-r-encrypted,8000,100,30,3,,, +Llama-xLAM-2-8b-fc-r-encrypted,8000,100,50,5,,, +Llama-xLAM-2-8b-fc-r-encrypted,8000,100,80,8,,, +Llama-xLAM-2-8b-fc-r-encrypted,8000,100,160,16,,, +Llama-xLAM-2-8b-fc-r-encrypted,8000,100,320,32,,, +Llama-xLAM-2-8b-fc-r-encrypted,16000,100,10,1,,, +Llama-xLAM-2-8b-fc-r-encrypted,16000,100,30,3,,, +Llama-xLAM-2-8b-fc-r-encrypted,16000,100,50,5,,, +Llama-xLAM-2-8b-fc-r-encrypted,16000,100,80,8,,, \ No newline at end of file diff --git a/benchmarking/benchmarking_bundles/datakrypto-complete-non_encrypted-model_configs.csv b/benchmarking/benchmarking_bundles/datakrypto-complete-non_encrypted-model_configs.csv new file mode 100644 index 000000000..5d6e2d144 --- /dev/null +++ b/benchmarking/benchmarking_bundles/datakrypto-complete-non_encrypted-model_configs.csv @@ -0,0 +1,17 @@ +model_name,input_tokens,output_tokens,num_requests,concurrent_requests,qps,qps_distribution,multimodal_img_size +Llama-xLAM-2-8b-fc-r,3900,100,10,1,,, +Llama-xLAM-2-8b-fc-r,3900,100,30,3,,, +Llama-xLAM-2-8b-fc-r,3900,100,50,5,,, +Llama-xLAM-2-8b-fc-r,3900,100,80,8,,, +Llama-xLAM-2-8b-fc-r,3900,100,160,16,,, +Llama-xLAM-2-8b-fc-r,3900,100,320,32,,, +Llama-xLAM-2-8b-fc-r,8000,100,10,1,,, +Llama-xLAM-2-8b-fc-r,8000,100,30,3,,, +Llama-xLAM-2-8b-fc-r,8000,100,50,5,,, +Llama-xLAM-2-8b-fc-r,8000,100,80,8,,, +Llama-xLAM-2-8b-fc-r,8000,100,160,16,,, +Llama-xLAM-2-8b-fc-r,8000,100,320,32,,, +Llama-xLAM-2-8b-fc-r,16000,100,10,1,,, +Llama-xLAM-2-8b-fc-r,16000,100,30,3,,, +Llama-xLAM-2-8b-fc-r,16000,100,50,5,,, +Llama-xLAM-2-8b-fc-r,16000,100,80,8,,, \ No newline at end of file From 7de968433c331b329061625c88583bfaa06feff6 Mon Sep 17 00:00:00 2001 From: Rodrigo Maldonado Date: Fri, 8 May 2026 11:24:39 -0500 Subject: [PATCH 15/17] last run --- benchmarking/benchmarking_bundles/config.yaml | 4 +-- .../model_configs_example.csv | 28 +++++++++++-------- 2 files changed, 18 insertions(+), 14 deletions(-) diff --git a/benchmarking/benchmarking_bundles/config.yaml b/benchmarking/benchmarking_bundles/config.yaml index 13a2b5e7f..a44af786a 100644 --- a/benchmarking/benchmarking_bundles/config.yaml +++ b/benchmarking/benchmarking_bundles/config.yaml @@ -1,7 +1,7 @@ model_configs_path: '../benchmarking/benchmarking_bundles/model_configs_example.csv' llm_api: 'sncloud' -output_files_dir: '../benchmarking/data/bundle_tests/datakrypto/speed_bench_test_encrypted_multiple' -consolidated_results_dir: '../benchmarking/data/bundle_tests/datakrypto/speed_bench_test_encrypted_multiple/consolidated_results' +output_files_dir: '../benchmarking/data/datakrypto/high_ss/speed_bench_test_encrypted_multiple' +consolidated_results_dir: '../benchmarking/data/datakrypto/high_ss/speed_bench_test_encrypted_multiple/consolidated_results' timeout: 3600 time_delay: 0 # Row-level concurrency diff --git a/benchmarking/benchmarking_bundles/model_configs_example.csv b/benchmarking/benchmarking_bundles/model_configs_example.csv index a7391adf0..acfaa92ab 100644 --- a/benchmarking/benchmarking_bundles/model_configs_example.csv +++ b/benchmarking/benchmarking_bundles/model_configs_example.csv @@ -1,13 +1,17 @@ model_name,input_tokens,output_tokens,num_requests,concurrent_requests,qps,qps_distribution,multimodal_img_size -Llama-xLAM-2-8b-fc-r-encrypted,2000,100,10,1,,, -Llama-xLAM-2-8b-fc-r-encrypted,2000,100,20,2,,, -Llama-xLAM-2-8b-fc-r-encrypted,2000,100,40,4,,, -Llama-xLAM-2-8b-fc-r-encrypted,2000,100,80,8,,, -Llama-xLAM-2-8b-fc-r-encrypted,2000,100,160,16,,, -Llama-xLAM-2-8b-fc-r-encrypted,2000,100,320,32,,, -Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,10,1,,, -Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,20,2,,, -Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,40,4,,, -Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,80,8,,, -Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,160,16,,, -Llama-xLAM-2-8b-fc-r-encrypted,2000,1024,320,32,,, \ No newline at end of file +Llama-xLAM-2-8b-fc-r,3900,100,10,1,,, +Llama-xLAM-2-8b-fc-r,3900,100,30,3,,, +Llama-xLAM-2-8b-fc-r,3900,100,50,5,,, +Llama-xLAM-2-8b-fc-r,3900,100,80,8,,, +Llama-xLAM-2-8b-fc-r,3900,100,160,16,,, +Llama-xLAM-2-8b-fc-r,3900,100,320,32,,, +Llama-xLAM-2-8b-fc-r,8000,100,10,1,,, +Llama-xLAM-2-8b-fc-r,8000,100,30,3,,, +Llama-xLAM-2-8b-fc-r,8000,100,50,5,,, +Llama-xLAM-2-8b-fc-r,8000,100,80,8,,, +Llama-xLAM-2-8b-fc-r,8000,100,160,16,,, +Llama-xLAM-2-8b-fc-r,8000,100,320,32,,, +Llama-xLAM-2-8b-fc-r,16000,100,10,1,,, +Llama-xLAM-2-8b-fc-r,16000,100,30,3,,, +Llama-xLAM-2-8b-fc-r,16000,100,50,5,,, +Llama-xLAM-2-8b-fc-r,16000,100,80,8,,, \ No newline at end of file From 12e6697b220ae8176a6e0c238b191c107dbf3cdb Mon Sep 17 00:00:00 2001 From: Rodrigo Maldonado Date: Fri, 8 May 2026 11:25:00 -0500 Subject: [PATCH 16/17] updated notebook with break up by input size --- ...-performance-analysis-complete-tests.ipynb | 308 ++++++++++++++++++ 1 file changed, 308 insertions(+) create mode 100644 benchmarking/benchmarking_bundles/notebooks/datakrypto-tee-performance-analysis-complete-tests.ipynb diff --git a/benchmarking/benchmarking_bundles/notebooks/datakrypto-tee-performance-analysis-complete-tests.ipynb b/benchmarking/benchmarking_bundles/notebooks/datakrypto-tee-performance-analysis-complete-tests.ipynb new file mode 100644 index 000000000..671863f0a --- /dev/null +++ b/benchmarking/benchmarking_bundles/notebooks/datakrypto-tee-performance-analysis-complete-tests.ipynb @@ -0,0 +1,308 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "cell-setup", + "metadata": {}, + "outputs": [], + "source": "import os\nimport sys\nimport warnings\nimport numpy as np\nimport pandas as pd\nimport plotly.express as px\nimport plotly.graph_objects as go\nfrom plotly.subplots import make_subplots\n\nwarnings.filterwarnings('ignore')\npd.set_option('display.float_format', '{:.4f}'.format)\npd.set_option('display.max_columns', 50)\n\nfor rel_path in ['../', '../../', '../../../']:\n abs_path = os.path.abspath(rel_path)\n if abs_path not in sys.path:\n sys.path.insert(0, abs_path)\n\nfrom benchmarking.utils import read_perf_eval_json_files\n\nCOLOR_MAP = {'Encrypted': '#EF553B', 'Non-Encrypted': '#636EFA'}\nSYMBOL_MAP = {'Encrypted': 'circle', 'Non-Encrypted': 'square'}\nCONC_ORDER = [1, 3, 5, 8, 16, 32]\nCONC_STR = [str(c) for c in CONC_ORDER]\nINPUT_ORDER = [3900, 8000, 16000]\nINPUT_LABELS = [f'{n:,} input tokens' for n in INPUT_ORDER]\n\nprint('Setup complete')" + }, + { + "cell_type": "markdown", + "id": "cell-title", + "metadata": {}, + "source": "# DataKrypto TEE Performance Analysis\n## Encrypted vs Non-Encrypted Model — Benchmarking Report\n\n---\n\n### Architecture\n\n```\nNon-Encrypted:\n Client ──────────────────────────► SambaNova Inference Server\n (1 network hop)\n\nEncrypted:\n Client ──► DataKrypto TEE ───────► SambaNova Inference Server\n │ encrypts input returns encrypted chunks\n │ decrypts each output ◄─────────────────────────┘\n │ chunk (streaming)\n └──► Client\n (2 network hops total)\n```\n\n### Models\n| Model | Description |\n|-------|-------------|\n| `Llama-xLAM-2-8b-fc-r` | Base model (plain inference) |\n| `Llama-xLAM-2-8b-fc-r-encrypted` | Runs through DataKrypto TEE |\n\n> Note: The two models are served by **separate SambaNova endpoints** which may have\n> different hardware allocations or decode configurations. Any baseline performance\n> difference between them is an endpoint-level characteristic — not caused by the TEE.\n\n### Test Matrix\n- **Input tokens**: 3,900 / 8,000 / 16,000 (varied — primary axis of analysis)\n- **Output tokens**: 100 (fixed)\n- **Concurrencies**: 1, 3, 5, 8, 16, 32\n- Each concurrency level = **N separate HTTP requests sent simultaneously** (not a single batch API call — N independent requests hitting the server at the same time, which the server can dynamically batch together for inference)\n- **Multi Run (10x)**: 10 sequential rounds of N simultaneous requests — provides statistical robustness and tests sustained server/TEE behavior over time\n\n> All plots in this report are **faceted by Input Tokens** so encryption / network / server-side\n> behavior can be compared directly across prompt sizes.\n\n### TEE Overhead Sources\n1. **Input Encryption** — encrypts the prompt (grows with input token count)\n2. **Output Decryption** — decrypts each streaming token chunk (grows with output tokens)\n3. **Network Hop** — extra Client → TEE → Server roundtrip (dominant overhead)\n\n> **TEE capacity constraint**: The TEE has **8 cores** and processes encryption/decryption\n> sequentially per core. At concurrency > 8, the TEE queue fills up causing requests to wait,\n> which means concurrent requests may arrive at the inference server **staggered** rather than\n> simultaneously — reducing the effective batch size the server sees.\n\n---" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-load", + "metadata": {}, + "outputs": [], + "source": "DATA_ROOT = os.path.abspath('../../data/datakrypto')\n\nDATASETS = {\n 'enc_multiple': {\n 'consolidated': f'{DATA_ROOT}/high_ss/speed_bench_test_encrypted_multiple/consolidated_results/20260506-120053.565163-complete.xlsx',\n 'run_dir': f'{DATA_ROOT}/high_ss/speed_bench_test_encrypted_multiple/20260506-120053.565163-complete',\n 'encrypted': True,\n 'run_type': 'Multi Run (10x)',\n 'label': 'Encrypted',\n },\n 'plain_multiple': {\n 'consolidated': f'{DATA_ROOT}/high_ss/speed_bench_test_nonencrypted_multiple/consolidated_results/20260506-121009.676284.xlsx',\n 'run_dir': f'{DATA_ROOT}/high_ss/speed_bench_test_nonencrypted_multiple/20260506-121009.676284',\n 'encrypted': False,\n 'run_type': 'Multi Run (10x)',\n 'label': 'Non-Encrypted',\n },\n}\n\ndfs = []\nfor key, meta in DATASETS.items():\n df = pd.read_excel(meta['consolidated'])\n df['dataset_key'] = key\n df['encrypted'] = meta['encrypted']\n df['run_type'] = meta['run_type']\n df['label'] = meta['label']\n dfs.append(df)\n\ndf_all = pd.concat(dfs, ignore_index=True)\n\n# Derived columns — input-token labels are now the primary facet\ndf_all['in_tokens_label'] = df_all['num_input_tokens'].apply(lambda n: f'{int(n):,} input tokens')\ndf_all['out_tokens_label'] = df_all['num_output_tokens'].astype(str) + ' output tokens'\ndf_all['total_requests'] = df_all['num_completed_requests'] + df_all['number_errors']\ndf_all['error_rate_pct'] = (df_all['number_errors'] / df_all['total_requests'].replace(0, np.nan)) * 100\ndf_all['completion_rate_pct'] = 100 - df_all['error_rate_pct']\n\n# Ordered categoricals for consistent facet ordering\ndf_all['in_tokens_label'] = pd.Categorical(\n df_all['in_tokens_label'], categories=INPUT_LABELS, ordered=True\n)\n\nprint(f'Loaded {len(df_all)} rows across {df_all[\"dataset_key\"].nunique()} datasets')\nprint(f'Input tokens: {sorted(df_all[\"num_input_tokens\"].unique())}')\nprint(f'Output tokens: {sorted(df_all[\"num_output_tokens\"].unique())}')\nprint(f'Concurrencies: {sorted(df_all[\"num_concurrent_requests\"].unique())}')\ndf_all.groupby(['label', 'in_tokens_label'])[['num_concurrent_requests']].count().rename(\n columns={'num_concurrent_requests': 'scenarios'}\n)" + }, + { + "cell_type": "markdown", + "id": "cell-overview-hdr", + "metadata": {}, + "source": [ + "## 1. Dataset Overview" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-overview", + "metadata": {}, + "outputs": [], + "source": "cols = ['label',\n 'num_input_tokens',\n 'num_output_tokens',\n 'num_concurrent_requests',\n 'num_completed_requests',\n 'number_errors',\n 'error_rate_pct'\n ]\noverview = df_all[cols].copy()\noverview.columns = ['Model',\n 'Input Tokens',\n 'Output Tokens',\n 'Concurrency',\n 'Completed',\n 'Errors',\n 'Error Rate (%)'\n ]\noverview['Error Rate (%)'] = overview['Error Rate (%)'].fillna(0).round(1)\noverview = overview.sort_values(['Input Tokens', 'Model', 'Concurrency'])\n\ndef _highlight_errors(row):\n if row['Errors'] > 0:\n return ['background-color: #ffdddd'] * len(row)\n return [''] * len(row)\n\noverview.style.apply(_highlight_errors, axis=1).format({'Error Rate (%)': '{:.1f}%'})" + }, + { + "cell_type": "markdown", + "id": "cell-errors-hdr", + "metadata": {}, + "source": [ + "## 2. Error & Completion Rate Analysis\n", + "\n", + "The TEE has **8 cores** processing encryption/decryption sequentially.\n", + "At concurrency > 8, the TEE queue fills up and requests may time out or be rejected,\n", + "producing errors on the encrypted model only. The plain model should show zero errors." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-errors-chart", + "metadata": {}, + "outputs": [], + "source": "fig = px.bar(\n df_all,\n x='num_concurrent_requests',\n y='error_rate_pct',\n color='label',\n facet_col='in_tokens_label',\n barmode='group',\n color_discrete_map=COLOR_MAP,\n category_orders={\n 'num_concurrent_requests': CONC_ORDER,\n 'in_tokens_label': INPUT_LABELS,\n },\n labels={\n 'num_concurrent_requests': 'Concurrency',\n 'error_rate_pct': 'Error Rate (%)',\n 'label': 'Model',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'Request Error Rate by Concurrency — faceted by Input Tokens
'\n 'Errors appear on Encrypted model at concurrency > 8 (TEE queue saturation). '\n 'Non-Encrypted should be 0% throughout.'\n ),\n)\nfig.update_layout(height=420, width=1200, template='plotly_white')\nfig.update_xaxes(type='category')\nfig.show()" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-reqmin-chart", + "metadata": {}, + "outputs": [], + "source": "# Completed requests per minute — real system throughput capacity\nfig = px.line(\n df_all,\n x='num_concurrent_requests',\n y='num_completed_requests_per_min',\n color='label',\n symbol='label',\n facet_col='in_tokens_label',\n color_discrete_map=COLOR_MAP,\n symbol_map=SYMBOL_MAP,\n log_x=True,\n markers=True,\n category_orders={'in_tokens_label': INPUT_LABELS},\n labels={\n 'num_concurrent_requests': 'Concurrency (log scale)',\n 'num_completed_requests_per_min': 'Completed Requests / min',\n 'label': 'Model',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'System Throughput: Completed Requests per Minute — faceted by Input Tokens
'\n 'Indicator of system capacity. '\n 'Encrypted throughput is increasingly limited by TEE queueing as input tokens grow '\n '(more bytes to encrypt) and at higher concurrencies.'\n ),\n)\nfig.update_layout(height=450, width=1200, template='plotly_white')\nfig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\nfig.show()" + }, + { + "cell_type": "markdown", + "id": "cell-server-hdr", + "metadata": {}, + "source": "## 3. Server-Side Metrics — Behavior Across Input Sizes\n\nServer-side metrics measure what happens **inside the SambaNova engine**, after the TEE has\nrelayed the request. They tell us whether the TEE itself slows the model — and they isolate\nthe **input-size effect** on prefill vs. decode.\n\n**What to look for as input tokens grow (3,900 → 8,000 → 16,000):**\n- **Server TTFT** rises (prefill scales with input length)\n- **Server tok/s** for decode should be roughly stable (decode rate is mostly independent\n of input length once prefill is done)\n- **Server E2E latency** rises mainly because of prefill, not because of the TEE\n\n**TEE de-batching effect:** because the TEE processes encryption sequentially per core,\nconcurrent requests reach the inference server staggered. This often keeps the encrypted path's\n**effective server batch size smaller**, so its server-side TTFT can be lower than the\nnon-encrypted path at high concurrency." + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-server-ttft", + "metadata": {}, + "outputs": [], + "source": "fig = px.line(\n df_all,\n x='num_concurrent_requests',\n y='server_ttft_s_p50',\n color='label',\n symbol='label',\n facet_col='in_tokens_label',\n color_discrete_map=COLOR_MAP,\n symbol_map=SYMBOL_MAP,\n log_x=True,\n markers=True,\n category_orders={'in_tokens_label': INPUT_LABELS},\n labels={\n 'num_concurrent_requests': 'Concurrency (log scale)',\n 'server_ttft_s_p50': 'Server TTFT p50 (s)',\n 'label': 'Model',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'Server Time to First Token — p50 (faceted by Input Tokens)
'\n 'TTFT grows with input size (longer prefill). Watch for the de-batching effect: '\n 'at high concurrency, encrypted server TTFT can be lower than non-encrypted because '\n 'staggered TEE arrivals shrink the effective server batch.'\n ),\n)\nfig.update_layout(height=450, width=1200, template='plotly_white')\nfig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\nfig.show()" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-server-toks", + "metadata": {}, + "outputs": [], + "source": "fig = px.line(\n df_all,\n x='num_concurrent_requests',\n y='server_output_token_per_s_p50',\n color='label',\n symbol='label',\n facet_col='in_tokens_label',\n color_discrete_map=COLOR_MAP,\n symbol_map=SYMBOL_MAP,\n log_x=True,\n markers=True,\n category_orders={'in_tokens_label': INPUT_LABELS},\n labels={\n 'num_concurrent_requests': 'Concurrency (log scale)',\n 'server_output_token_per_s_p50': 'Server Output Tok/s p50 (per request)',\n 'label': 'Model',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'Server Output Throughput per Request — p50 (faceted by Input Tokens)
'\n 'Decode rate should be roughly independent of input size. Any large gap between '\n 'encrypted and non-encrypted in the same facet would point at endpoint configuration '\n 'differences, not the TEE itself.'\n ),\n)\nfig.update_layout(height=450, width=1200, template='plotly_white')\nfig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\nfig.show()" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-server-e2e", + "metadata": {}, + "outputs": [], + "source": "fig = px.line(\n df_all,\n x='num_concurrent_requests',\n y='server_end_to_end_latency_s_p50',\n color='label',\n symbol='label',\n facet_col='in_tokens_label',\n color_discrete_map=COLOR_MAP,\n symbol_map=SYMBOL_MAP,\n log_x=True,\n markers=True,\n category_orders={'in_tokens_label': INPUT_LABELS},\n labels={\n 'num_concurrent_requests': 'Concurrency (log scale)',\n 'server_end_to_end_latency_s_p50': 'Server E2E Latency p50 (s)',\n 'label': 'Model',\n 'in_tokens_label': 'Input Tokens',\n },\n title='Server End-to-End Latency — p50 (faceted by Input Tokens)',\n)\nfig.update_layout(height=450, width=1200, template='plotly_white')\nfig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\nfig.show()" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-server-delta", + "metadata": {}, + "outputs": [], + "source": "# Server metrics delta table — Encrypted minus Non-Encrypted (per Input Tokens × Concurrency)\nDIMS = ['num_input_tokens', 'num_output_tokens', 'num_concurrent_requests']\n\ndf_enc_s = df_all[df_all['label'] == 'Encrypted'].set_index(DIMS)\ndf_plain_s = df_all[df_all['label'] == 'Non-Encrypted'].set_index(DIMS)\n\nserver_metrics = [\n ('server_ttft_s_p50', 'Server TTFT p50 (s)'),\n ('server_output_token_per_s_p50', 'Server Tok/s p50'),\n ('server_end_to_end_latency_s_p50','Server E2E p50 (s)'),\n]\n\ndelta_rows = []\nfor col, label in server_metrics:\n if col not in df_enc_s.columns:\n continue\n for idx in df_enc_s.index:\n if idx not in df_plain_s.index:\n continue\n enc_val = df_enc_s.loc[idx, col]\n plain_val = df_plain_s.loc[idx, col]\n if pd.isna(enc_val) or pd.isna(plain_val):\n continue\n delta = enc_val - plain_val\n pct_chg = delta / plain_val * 100 if plain_val != 0 else np.nan\n delta_rows.append({\n 'Metric': label,\n 'Input Tokens': idx[0],\n 'Output Tokens': idx[1],\n 'Concurrency': idx[2],\n 'Non-Encrypted': plain_val,\n 'Encrypted': enc_val,\n 'Delta': delta,\n '% Change': pct_chg,\n })\n\ndf_server_delta = pd.DataFrame(delta_rows)\ndf_server_delta = df_server_delta.sort_values(['Metric', 'Input Tokens', 'Concurrency'])\n\ndef _pct_color(val):\n if pd.isna(val): return ''\n if val > 50: return 'background-color: #ffaaaa'\n if val > 20: return 'background-color: #ffd8aa'\n if val > 0: return 'background-color: #fffacc'\n if val < -20: return 'background-color: #aaffaa'\n return ''\n\ndf_server_delta.style \\\n .applymap(_pct_color, subset=['% Change']) \\\n .format({\n 'Non-Encrypted': '{:.4f}',\n 'Encrypted': '{:.4f}',\n 'Delta': '{:+.4f}',\n '% Change': '{:+.1f}%',\n })" + }, + { + "cell_type": "markdown", + "id": "cell-tee-hdr", + "metadata": {}, + "source": "## 4. TEE Overhead Decomposition (Encrypted Model Only)\n\nThree measurable components are returned by the TEE per request:\n\n| Component | Description | Expected Behavior vs Input Tokens |\n|-----------|-------------|----------------------------------|\n| **Input Encryption** | Time to encrypt the prompt before sending to server | grows with input token count |\n| **Output Decryption** | Cumulative time to decrypt all streaming output chunks | ~constant (output is fixed at 100) |\n| **Network Hop** | Round-trip latency through the TEE (Client→TEE→Server→TEE→Client) | grows with concurrency (TEE queue) and payload size |\n\n> Faceting by input tokens lets us see how much of the TEE overhead is driven by **prompt size**\n> as opposed to concurrency." + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-tee-stack", + "metadata": {}, + "outputs": [], + "source": "df_tee = df_all[df_all['label'] == 'Encrypted'].copy()\n\n# Convert ms → s for consistent units with latency charts\ndf_tee['Network Hop'] = df_tee['server_network_latency_ms_mean'] / 1000\ndf_tee['Input Encryption'] = df_tee['total_encryption_time_ms_mean'] / 1000\ndf_tee['Output Decryption'] = df_tee['total_decryption_time_ms_mean'] / 1000\n\nTEE_ID_COLS = ['in_tokens_label', 'num_concurrent_requests']\nTEE_VAL_COLS = ['Network Hop', 'Input Encryption', 'Output Decryption']\n\ndf_tee_melt = df_tee[TEE_ID_COLS + TEE_VAL_COLS].melt(\n id_vars=TEE_ID_COLS, var_name='Component', value_name='Overhead (s)'\n)\n\nCOMP_COLORS = {\n 'Network Hop': '#FF7F0E',\n 'Input Encryption': '#D62728',\n 'Output Decryption': '#2CA02C',\n}\n\nfig = px.bar(\n df_tee_melt,\n x='num_concurrent_requests',\n y='Overhead (s)',\n color='Component',\n facet_col='in_tokens_label',\n barmode='stack',\n color_discrete_map=COMP_COLORS,\n category_orders={\n 'num_concurrent_requests': CONC_ORDER,\n 'in_tokens_label': INPUT_LABELS,\n },\n labels={\n 'num_concurrent_requests': 'Concurrency',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'TEE Overhead Breakdown per Request — faceted by Input Tokens
'\n 'Network Hop dominates. Encryption grows with input size (more bytes to encrypt). '\n 'Decryption is roughly constant since output tokens are fixed at 100.'\n ),\n)\nfig.update_layout(height=450, width=1200, template='plotly_white')\nfig.update_xaxes(type='category')\nfig.show()" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-network-latency", + "metadata": {}, + "outputs": [], + "source": "# Network latency growth — most important overhead component\nfig = px.line(\n df_tee,\n x='num_concurrent_requests',\n y='server_network_latency_ms_mean',\n color='in_tokens_label',\n facet_col='in_tokens_label',\n log_x=True,\n markers=True,\n category_orders={'in_tokens_label': INPUT_LABELS},\n labels={\n 'num_concurrent_requests': 'Concurrency (log scale)',\n 'server_network_latency_ms_mean': 'TEE Network Latency — Mean (ms)',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'TEE Network Latency vs Concurrency — faceted by Input Tokens
'\n 'Grows with concurrency (TEE queueing) AND with input tokens '\n '(larger encrypted payload to ship).'\n ),\n)\nfig.update_layout(height=420, width=1200, template='plotly_white')\nfig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\nfig.show()" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-enc-dec-times", + "metadata": {}, + "outputs": [], + "source": "# Encryption and decryption times detail — one line per Input Tokens\nfig = make_subplots(\n rows=1, cols=2,\n subplot_titles=['Input Encryption Time (ms)', 'Output Decryption Time (ms)'],\n)\n\n_input_colors = {3900: '#1f77b4', 8000: '#2ca02c', 16000: '#ff7f0e'}\n_shown = set()\n\nfor in_tok, color in _input_colors.items():\n sub = df_tee[df_tee['num_input_tokens'] == in_tok].sort_values('num_concurrent_requests')\n name = f'{in_tok:,} input tokens'\n show = name not in _shown\n kw = dict(x=sub['num_concurrent_requests'], mode='lines+markers',\n name=name, line=dict(color=color),\n legendgroup=name, showlegend=show)\n fig.add_trace(go.Scatter(y=sub['total_encryption_time_ms_mean'], **kw), row=1, col=1)\n kw['showlegend'] = False\n fig.add_trace(go.Scatter(y=sub['total_decryption_time_ms_mean'], **kw), row=1, col=2)\n _shown.add(name)\n\nfig.update_xaxes(\n type='log', tickvals=CONC_ORDER, ticktext=CONC_STR,\n title_text='Concurrency (log scale)',\n)\nfig.update_yaxes(title_text='Time (ms)', col=1)\nfig.update_yaxes(title_text='Time (ms)', col=2)\nfig.update_layout(\n height=420, width=1200, template='plotly_white',\n legend_title='Input Tokens',\n title=(\n 'TEE Encryption & Decryption Times — by Input Tokens
'\n 'Encryption scales with input size (more bytes per prompt). '\n 'Decryption is roughly flat across input sizes since output is fixed at 100 tokens.'\n ),\n)\nfig.show()" + }, + { + "cell_type": "markdown", + "id": "cell-client-hdr", + "metadata": {}, + "source": [ + "## 5. Client-Side Metrics — End-User Experience\n", + "\n", + "Client metrics include the full roundtrip: **Client → (TEE) → Server → (TEE) → Client**.\n", + "\n", + "The gap between encrypted and non-encrypted client metrics is the\n", + "**observable user-facing cost** of the TEE encryption layer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-client-ttft", + "metadata": {}, + "outputs": [], + "source": "fig = px.line(\n df_all,\n x='num_concurrent_requests',\n y='client_ttft_s_p50',\n color='label',\n symbol='label',\n facet_col='in_tokens_label',\n color_discrete_map=COLOR_MAP,\n symbol_map=SYMBOL_MAP,\n log_x=True,\n markers=True,\n category_orders={'in_tokens_label': INPUT_LABELS},\n labels={\n 'num_concurrent_requests': 'Concurrency (log scale)',\n 'client_ttft_s_p50': 'Client TTFT p50 (s)',\n 'label': 'Model',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'Client Time to First Token — p50 (faceted by Input Tokens)
'\n 'Both models slow down as input grows (longer prefill). '\n 'Encrypted carries TEE network + encryption overhead on top — gap typically widens '\n 'with concurrency, but de-batching can flip it at high concurrency on smaller prompts.'\n ),\n)\nfig.update_layout(height=450, width=1200, template='plotly_white')\nfig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\nfig.show()" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-ttft-attribution", + "metadata": {}, + "outputs": [], + "source": "# Client TTFT overhead attribution: stacked TEE components vs total delta\nDIMS = ['num_input_tokens', 'num_output_tokens', 'num_concurrent_requests']\n\ndf_enc_a = df_all[df_all['label'] == 'Encrypted'].copy()\ndf_plain_a = df_all[df_all['label'] == 'Non-Encrypted'][DIMS + ['client_ttft_s_p50']].rename(\n columns={'client_ttft_s_p50': 'client_ttft_plain'}\n)\ndf_attr = df_enc_a.merge(df_plain_a, on=DIMS)\n\ndf_attr['client_ttft_delta'] = df_attr['client_ttft_s_p50'] - df_attr['client_ttft_plain']\ndf_attr['Network Hop (s)'] = df_attr['server_network_latency_ms_mean'] / 1000\ndf_attr['Input Encryption (s)'] = df_attr['total_encryption_time_ms_mean'] / 1000\ndf_attr['Output Decryption (s)']= df_attr['total_decryption_time_ms_mean'] / 1000\ndf_attr['Residual (s)'] = (\n df_attr['client_ttft_delta']\n - df_attr['Network Hop (s)']\n - df_attr['Input Encryption (s)']\n - df_attr['Output Decryption (s)']\n).clip(lower=0)\n\nATTR_COLS = ['Network Hop (s)', 'Input Encryption (s)', 'Output Decryption (s)', 'Residual (s)']\nATTR_COLORS = {\n 'Network Hop (s)': '#FF7F0E',\n 'Input Encryption (s)': '#D62728',\n 'Output Decryption (s)': '#2CA02C',\n 'Residual (s)': '#9467BD',\n}\n\ndf_attr_melt = df_attr.melt(\n id_vars=['in_tokens_label', 'num_concurrent_requests', 'client_ttft_delta'],\n value_vars=ATTR_COLS, var_name='Component', value_name='Overhead (s)',\n).dropna(subset=['Overhead (s)'])\n\nfig = px.bar(\n df_attr_melt,\n x='num_concurrent_requests',\n y='Overhead (s)',\n color='Component',\n facet_col='in_tokens_label',\n barmode='stack',\n color_discrete_map=ATTR_COLORS,\n category_orders={\n 'num_concurrent_requests': CONC_ORDER,\n 'in_tokens_label': INPUT_LABELS,\n },\n labels={\n 'num_concurrent_requests': 'Concurrency',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'Client TTFT Overhead Attribution: Encrypted − Non-Encrypted Delta — by Input Tokens
'\n 'Stacked bars = measured TEE components. Total bar height = Enc TTFT − Plain TTFT. '\n 'Residual = delta not fully explained by TEE metrics (measurement noise / endpoint diff).'\n ),\n)\nfig.update_layout(height=450, width=1200, template='plotly_white')\nfig.update_xaxes(type='category')\nfig.show()" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-client-e2e", + "metadata": {}, + "outputs": [], + "source": "fig = px.line(\n df_all,\n x='num_concurrent_requests',\n y='client_end_to_end_latency_s_p50',\n color='label',\n symbol='label',\n facet_col='in_tokens_label',\n color_discrete_map=COLOR_MAP,\n symbol_map=SYMBOL_MAP,\n log_x=True,\n markers=True,\n category_orders={'in_tokens_label': INPUT_LABELS},\n labels={\n 'num_concurrent_requests': 'Concurrency (log scale)',\n 'client_end_to_end_latency_s_p50': 'Client E2E Latency p50 (s)',\n 'label': 'Model',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'Client End-to-End Latency — p50 (faceted by Input Tokens)
'\n 'Full request duration as observed by the client. '\n 'Both prefill (input size) and TEE queueing (concurrency) push this up.'\n ),\n)\nfig.update_layout(height=450, width=1200, template='plotly_white')\nfig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\nfig.show()" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-client-toks", + "metadata": {}, + "outputs": [], + "source": "# Per-request client generation speed\nfig = px.line(\n df_all,\n x='num_concurrent_requests',\n y='client_output_token_per_s_p50',\n color='label',\n symbol='label',\n facet_col='in_tokens_label',\n color_discrete_map=COLOR_MAP,\n symbol_map=SYMBOL_MAP,\n log_x=True,\n markers=True,\n category_orders={'in_tokens_label': INPUT_LABELS},\n labels={\n 'num_concurrent_requests': 'Concurrency (log scale)',\n 'client_output_token_per_s_p50': 'Client Output Tok/s p50 (per request)',\n 'label': 'Model',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'Client Output Throughput per Request — p50 (faceted by Input Tokens)
'\n 'After the first token, subsequent tokens stream at this rate. '\n 'Encrypted is bounded by TEE streaming decryption; '\n 'Non-Encrypted by server generation at high concurrency.'\n ),\n)\nfig.update_layout(height=450, width=1200, template='plotly_white')\nfig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\nfig.show()" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-total-throughput", + "metadata": {}, + "outputs": [], + "source": "# Total system throughput — aggregate across all concurrent requests\nfig = px.line(\n df_all,\n x='num_concurrent_requests',\n y='client_total_output_throughput',\n color='label',\n symbol='label',\n facet_col='in_tokens_label',\n color_discrete_map=COLOR_MAP,\n symbol_map=SYMBOL_MAP,\n log_x=True,\n markers=True,\n category_orders={'in_tokens_label': INPUT_LABELS},\n labels={\n 'num_concurrent_requests': 'Concurrency (log scale)',\n 'client_total_output_throughput': 'Total Output Throughput (tok/s)',\n 'label': 'Model',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'Total Client Output Throughput — All Concurrent Requests (faceted by Input Tokens)
'\n 'Non-Encrypted scales nearly linearly with concurrency. '\n 'Encrypted ceiling drops as input tokens grow because each request takes longer to '\n 'encrypt + ship through the TEE.'\n ),\n)\nfig.update_layout(height=450, width=1200, template='plotly_white')\nfig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\nfig.show()" + }, + { + "cell_type": "markdown", + "id": "cell-dist-hdr", + "metadata": {}, + "source": "## 6. Per-Request Distributions\n\nBox plots using individual request data from all 10 rounds, faceted by **Input Tokens**.\nError requests (where `error_code` is set) are excluded." + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-load-individual", + "metadata": {}, + "outputs": [], + "source": "def _parse_fname(fname):\n \"\"\"Extract (input_tokens, output_tokens, concurrency) from benchmark filename.\n\n Filename format:\n synthetic______stream__*\n \"\"\"\n base = os.path.basename(str(fname)).replace('_individual_responses.json', '')\n parts = base.split('_')\n try:\n return int(parts[3]), int(parts[4]), int(parts[5])\n except (IndexError, ValueError):\n return None, None, None\n\ndfs_ind = []\nfor key, meta in DATASETS.items():\n try:\n df_ind = read_perf_eval_json_files(meta['run_dir'], type='individual_responses')\n if df_ind.empty:\n continue\n df_ind['label'] = meta['label']\n df_ind['run_type'] = meta['run_type']\n parsed = df_ind['filename'].apply(lambda f: pd.Series(_parse_fname(f)))\n df_ind['num_input_tokens'] = parsed[0]\n df_ind['num_output_tokens'] = parsed[1]\n df_ind['num_concurrent_requests'] = parsed[2]\n dfs_ind.append(df_ind)\n except Exception as e:\n print(f'Warning: could not load {key}: {e}')\n\ndf_individual = pd.concat(dfs_ind, ignore_index=True)\ndf_individual = df_individual.dropna(\n subset=['num_input_tokens', 'num_output_tokens', 'num_concurrent_requests', 'client_ttft_s']\n)\ndf_individual['num_input_tokens'] = df_individual['num_input_tokens'].astype(int)\ndf_individual['num_output_tokens'] = df_individual['num_output_tokens'].astype(int)\ndf_individual['num_concurrent_requests'] = df_individual['num_concurrent_requests'].astype(int)\ndf_individual['in_tokens_label'] = df_individual['num_input_tokens'].apply(lambda n: f'{n:,} input tokens')\ndf_individual['concurrency_str'] = df_individual['num_concurrent_requests'].astype(str)\ndf_individual['is_error'] = df_individual['error_code'].notna()\ndf_individual['in_tokens_label'] = pd.Categorical(\n df_individual['in_tokens_label'], categories=INPUT_LABELS, ordered=True\n)\n\nprint(f'Loaded {len(df_individual):,} individual request records')\ndf_individual.groupby(['label', 'in_tokens_label', 'is_error'])[['client_ttft_s']].count().rename(\n columns={'client_ttft_s': 'count'}\n)" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-box-client-ttft", + "metadata": {}, + "outputs": [], + "source": "df_box = df_individual[~df_individual['is_error']].copy()\n\nfig = px.box(\n df_box,\n x='concurrency_str',\n y='client_ttft_s',\n color='label',\n facet_col='in_tokens_label',\n color_discrete_map=COLOR_MAP,\n category_orders={\n 'concurrency_str': CONC_STR,\n 'in_tokens_label': INPUT_LABELS,\n },\n points='outliers',\n labels={\n 'concurrency_str': 'Concurrency',\n 'client_ttft_s': 'Client TTFT (s)',\n 'label': 'Model',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'Client TTFT Distribution per Concurrency — faceted by Input Tokens
'\n 'Encrypted shows higher median AND wider spread due to variable TEE network latency. '\n 'Spread grows with both input size and concurrency.'\n ),\n)\nfig.update_layout(height=450, width=1200, template='plotly_white')\nfig.show()" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-box-server-ttft", + "metadata": {}, + "outputs": [], + "source": "fig = px.box(\n df_box,\n x='concurrency_str',\n y='server_ttft_s',\n color='label',\n facet_col='in_tokens_label',\n color_discrete_map=COLOR_MAP,\n category_orders={\n 'concurrency_str': CONC_STR,\n 'in_tokens_label': INPUT_LABELS,\n },\n points='outliers',\n labels={\n 'concurrency_str': 'Concurrency',\n 'server_ttft_s': 'Server TTFT (s)',\n 'label': 'Model',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'Server TTFT Distribution per Concurrency — faceted by Input Tokens
'\n 'Server-side distribution. At medium concurrency, encrypted may show LOWER server TTFT '\n 'because TEE de-batching reduces the effective batch size seen by the server.'\n ),\n)\nfig.update_layout(height=450, width=1200, template='plotly_white')\nfig.show()" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-scatter-tee", + "metadata": {}, + "outputs": [], + "source": "# Per-request: TEE network latency vs server TTFT (encrypted only), one column per Input Tokens\ndf_enc_ind = df_individual[\n (df_individual['label'] == 'Encrypted') &\n (~df_individual['is_error']) &\n (df_individual['server_network_latency_ms'].notna())\n].copy()\n\nfig = px.scatter(\n df_enc_ind,\n x='server_ttft_s',\n y='server_network_latency_ms',\n color='concurrency_str',\n facet_col='in_tokens_label',\n opacity=0.55,\n category_orders={\n 'concurrency_str': CONC_STR,\n 'in_tokens_label': INPUT_LABELS,\n },\n labels={\n 'server_ttft_s': 'Server TTFT (s)',\n 'server_network_latency_ms': 'TEE Network Latency (ms)',\n 'in_tokens_label': 'Input Tokens',\n 'concurrency_str': 'Concurrency',\n },\n title=(\n 'Per-Request: TEE Network Latency vs Server TTFT — Encrypted (faceted by Input Tokens)
'\n 'How network latency varies relative to server prefill time. '\n 'A flatter horizontal spread = network latency is independent of server load.'\n ),\n)\nfig.update_layout(height=420, width=1300, template='plotly_white')\nfig.show()" + }, + { + "cell_type": "markdown", + "id": "cell-summary-hdr", + "metadata": {}, + "source": [ + "## 7. Summary\n", + "\n", + "### Full Metric Comparison" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-summary-table", + "metadata": {}, + "outputs": [], + "source": "DIMS = ['num_input_tokens', 'num_output_tokens', 'num_concurrent_requests']\n\nALL_METRICS = [\n ('server_ttft_s_p50', 'Server TTFT p50 (s)'),\n ('server_output_token_per_s_p50', 'Server Tok/s p50'),\n ('server_end_to_end_latency_s_p50', 'Server E2E p50 (s)'),\n ('client_ttft_s_p50', 'Client TTFT p50 (s)'),\n ('client_end_to_end_latency_s_p50', 'Client E2E p50 (s)'),\n ('client_output_token_per_s_p50', 'Client Tok/s p50'),\n ('client_total_output_throughput', 'Total Throughput (tok/s)'),\n ('num_completed_requests_per_min', 'Req / min'),\n ('error_rate_pct', 'Error Rate (%)'),\n]\n\ndf_enc_s = df_all[df_all['label'] == 'Encrypted'].set_index(DIMS)\ndf_plain_s = df_all[df_all['label'] == 'Non-Encrypted'].set_index(DIMS)\n\nrows = []\nfor col, label in ALL_METRICS:\n if col not in df_enc_s.columns:\n continue\n for idx in df_enc_s.index:\n if idx not in df_plain_s.index:\n continue\n enc_v = df_enc_s.loc[idx, col]\n plain_v = df_plain_s.loc[idx, col]\n if pd.isna(enc_v) and pd.isna(plain_v):\n continue\n delta = enc_v - plain_v if pd.notna(enc_v) and pd.notna(plain_v) else np.nan\n pct_chg = delta / plain_v * 100 if pd.notna(delta) and plain_v != 0 else np.nan\n rows.append({\n 'Metric': label,\n 'Input Tokens': idx[0],\n 'Output Tokens': idx[1],\n 'Concurrency': idx[2],\n 'Non-Encrypted': plain_v,\n 'Encrypted': enc_v,\n 'Delta': delta,\n '% Change': pct_chg,\n })\n\ndf_summary = pd.DataFrame(rows).sort_values(\n ['Metric', 'Input Tokens', 'Concurrency']\n)\n\ndef _pct_color(val):\n if pd.isna(val): return ''\n if val > 100: return 'background-color: #ff6666'\n if val > 50: return 'background-color: #ffaaaa'\n if val > 20: return 'background-color: #ffd8aa'\n if val > 0: return 'background-color: #fffacc'\n if val < -20: return 'background-color: #aaffaa'\n return ''\n\ndf_summary.style \\\n .applymap(_pct_color, subset=['% Change']) \\\n .format({\n 'Non-Encrypted': '{:.3f}',\n 'Encrypted': '{:.3f}',\n 'Delta': '{:+.3f}',\n '% Change': '{:+.1f}%',\n }, na_rep='—')" + }, + { + "cell_type": "markdown", + "id": "cell-tee-overhead-hdr", + "metadata": {}, + "source": [ + "### TEE Overhead Attribution per Scenario" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-tee-overhead-table", + "metadata": {}, + "outputs": [], + "source": "DIMS = ['num_input_tokens', 'num_output_tokens', 'num_concurrent_requests']\n\ndf_tee_oh = df_all[df_all['label'] == 'Encrypted'].copy()\ndf_tee_oh = df_tee_oh.merge(\n df_all[df_all['label'] == 'Non-Encrypted'][DIMS + ['client_ttft_s_p50']].rename(\n columns={'client_ttft_s_p50': 'client_ttft_plain'}\n ),\n on=DIMS,\n)\n\ndf_tee_oh['enc_time_s'] = df_tee_oh['total_encryption_time_ms_mean'] / 1000\ndf_tee_oh['dec_time_s'] = df_tee_oh['total_decryption_time_ms_mean'] / 1000\ndf_tee_oh['network_s'] = df_tee_oh['server_network_latency_ms_mean'] / 1000\ndf_tee_oh['tee_total_s'] = df_tee_oh['enc_time_s'] + df_tee_oh['dec_time_s'] + df_tee_oh['network_s']\ndf_tee_oh['ttft_delta_s'] = df_tee_oh['client_ttft_s_p50'] - df_tee_oh['client_ttft_plain']\ndf_tee_oh['network_pct'] = df_tee_oh['network_s'] / df_tee_oh['client_ttft_s_p50'] * 100\ndf_tee_oh['enc_pct'] = df_tee_oh['enc_time_s'] / df_tee_oh['client_ttft_s_p50'] * 100\ndf_tee_oh['dec_pct'] = df_tee_oh['dec_time_s'] / df_tee_oh['client_ttft_s_p50'] * 100\ndf_tee_oh['tee_total_pct'] = df_tee_oh['tee_total_s'] / df_tee_oh['client_ttft_s_p50'] * 100\n\nDISPLAY = {\n 'num_input_tokens': 'Input Tokens',\n 'num_output_tokens': 'Output Tokens',\n 'num_concurrent_requests': 'Concurrency',\n 'client_ttft_plain': 'Plain TTFT (s)',\n 'client_ttft_s_p50': 'Enc TTFT (s)',\n 'ttft_delta_s': 'Delta (s)',\n 'enc_time_s': 'Encryption (s)',\n 'dec_time_s': 'Decryption (s)',\n 'network_s': 'Network Hop (s)',\n 'tee_total_s': 'TEE Total (s)',\n 'tee_total_pct': 'TEE % of Enc TTFT',\n 'network_pct': 'Network %',\n 'enc_pct': 'Encrypt %',\n 'dec_pct': 'Decrypt %',\n}\n\ndf_tee_display = (\n df_tee_oh[list(DISPLAY.keys())]\n .rename(columns=DISPLAY)\n .sort_values(['Input Tokens', 'Concurrency'])\n)\n\npct_cols = [c for c in df_tee_display.columns if '%' in c]\ns_cols = [c for c in df_tee_display.columns if '(s)' in c]\n\ndf_tee_display.style \\\n .background_gradient(subset=['TEE % of Enc TTFT'], cmap='YlOrRd') \\\n .format({c: '{:.1f}%' for c in pct_cols}) \\\n .format({c: '{:.4f}' for c in s_cols}, na_rep='—')" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56a895dd", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7cf9ba4", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dea813eb", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "benchmarking_venv", + "language": "python", + "name": "benchmarking_venv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From 556e4ddfd4e6667a812a7c75efd90470a3ac9588 Mon Sep 17 00:00:00 2001 From: Rodrigo Maldonado Date: Fri, 8 May 2026 14:25:50 -0500 Subject: [PATCH 17/17] last run using data from vm and ss up to 16k --- ...-performance-analysis-complete-tests.ipynb | 27200 +++++++++++++++- 1 file changed, 27126 insertions(+), 74 deletions(-) diff --git a/benchmarking/benchmarking_bundles/notebooks/datakrypto-tee-performance-analysis-complete-tests.ipynb b/benchmarking/benchmarking_bundles/notebooks/datakrypto-tee-performance-analysis-complete-tests.ipynb index 671863f0a..1f97de7b9 100644 --- a/benchmarking/benchmarking_bundles/notebooks/datakrypto-tee-performance-analysis-complete-tests.ipynb +++ b/benchmarking/benchmarking_bundles/notebooks/datakrypto-tee-performance-analysis-complete-tests.ipynb @@ -2,25 +2,252 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "cell-setup", "metadata": {}, - "outputs": [], - "source": "import os\nimport sys\nimport warnings\nimport numpy as np\nimport pandas as pd\nimport plotly.express as px\nimport plotly.graph_objects as go\nfrom plotly.subplots import make_subplots\n\nwarnings.filterwarnings('ignore')\npd.set_option('display.float_format', '{:.4f}'.format)\npd.set_option('display.max_columns', 50)\n\nfor rel_path in ['../', '../../', '../../../']:\n abs_path = os.path.abspath(rel_path)\n if abs_path not in sys.path:\n sys.path.insert(0, abs_path)\n\nfrom benchmarking.utils import read_perf_eval_json_files\n\nCOLOR_MAP = {'Encrypted': '#EF553B', 'Non-Encrypted': '#636EFA'}\nSYMBOL_MAP = {'Encrypted': 'circle', 'Non-Encrypted': 'square'}\nCONC_ORDER = [1, 3, 5, 8, 16, 32]\nCONC_STR = [str(c) for c in CONC_ORDER]\nINPUT_ORDER = [3900, 8000, 16000]\nINPUT_LABELS = [f'{n:,} input tokens' for n in INPUT_ORDER]\n\nprint('Setup complete')" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setup complete\n" + ] + } + ], + "source": [ + "import os\n", + "import sys\n", + "import warnings\n", + "import numpy as np\n", + "import pandas as pd\n", + "import plotly.express as px\n", + "import plotly.graph_objects as go\n", + "from plotly.subplots import make_subplots\n", + "\n", + "warnings.filterwarnings('ignore')\n", + "pd.set_option('display.float_format', '{:.4f}'.format)\n", + "pd.set_option('display.max_columns', 50)\n", + "\n", + "for rel_path in ['../', '../../', '../../../']:\n", + " abs_path = os.path.abspath(rel_path)\n", + " if abs_path not in sys.path:\n", + " sys.path.insert(0, abs_path)\n", + "\n", + "from benchmarking.utils import read_perf_eval_json_files\n", + "\n", + "COLOR_MAP = {'Encrypted': '#EF553B', 'Non-Encrypted': '#636EFA'}\n", + "SYMBOL_MAP = {'Encrypted': 'circle', 'Non-Encrypted': 'square'}\n", + "CONC_ORDER = [1, 3, 5, 8, 16, 32]\n", + "CONC_STR = [str(c) for c in CONC_ORDER]\n", + "INPUT_ORDER = [3900, 8000, 16000]\n", + "INPUT_LABELS = [f'{n:,} input tokens' for n in INPUT_ORDER]\n", + "\n", + "print('Setup complete')" + ] }, { "cell_type": "markdown", "id": "cell-title", "metadata": {}, - "source": "# DataKrypto TEE Performance Analysis\n## Encrypted vs Non-Encrypted Model — Benchmarking Report\n\n---\n\n### Architecture\n\n```\nNon-Encrypted:\n Client ──────────────────────────► SambaNova Inference Server\n (1 network hop)\n\nEncrypted:\n Client ──► DataKrypto TEE ───────► SambaNova Inference Server\n │ encrypts input returns encrypted chunks\n │ decrypts each output ◄─────────────────────────┘\n │ chunk (streaming)\n └──► Client\n (2 network hops total)\n```\n\n### Models\n| Model | Description |\n|-------|-------------|\n| `Llama-xLAM-2-8b-fc-r` | Base model (plain inference) |\n| `Llama-xLAM-2-8b-fc-r-encrypted` | Runs through DataKrypto TEE |\n\n> Note: The two models are served by **separate SambaNova endpoints** which may have\n> different hardware allocations or decode configurations. Any baseline performance\n> difference between them is an endpoint-level characteristic — not caused by the TEE.\n\n### Test Matrix\n- **Input tokens**: 3,900 / 8,000 / 16,000 (varied — primary axis of analysis)\n- **Output tokens**: 100 (fixed)\n- **Concurrencies**: 1, 3, 5, 8, 16, 32\n- Each concurrency level = **N separate HTTP requests sent simultaneously** (not a single batch API call — N independent requests hitting the server at the same time, which the server can dynamically batch together for inference)\n- **Multi Run (10x)**: 10 sequential rounds of N simultaneous requests — provides statistical robustness and tests sustained server/TEE behavior over time\n\n> All plots in this report are **faceted by Input Tokens** so encryption / network / server-side\n> behavior can be compared directly across prompt sizes.\n\n### TEE Overhead Sources\n1. **Input Encryption** — encrypts the prompt (grows with input token count)\n2. **Output Decryption** — decrypts each streaming token chunk (grows with output tokens)\n3. **Network Hop** — extra Client → TEE → Server roundtrip (dominant overhead)\n\n> **TEE capacity constraint**: The TEE has **8 cores** and processes encryption/decryption\n> sequentially per core. At concurrency > 8, the TEE queue fills up causing requests to wait,\n> which means concurrent requests may arrive at the inference server **staggered** rather than\n> simultaneously — reducing the effective batch size the server sees.\n\n---" + "source": [ + "# DataKrypto TEE Performance Analysis\n", + "## Encrypted vs Non-Encrypted Model — Benchmarking Report\n", + "\n", + "---\n", + "\n", + "### Architecture\n", + "\n", + "```\n", + "Non-Encrypted:\n", + " Client ──────────────────────────► SambaNova Inference Server\n", + " (1 network hop)\n", + "\n", + "Encrypted:\n", + " Client ──► DataKrypto TEE ───────► SambaNova Inference Server\n", + " │ encrypts input returns encrypted chunks\n", + " │ decrypts each output ◄─────────────────────────┘\n", + " │ chunk (streaming)\n", + " └──► Client\n", + " (2 network hops total)\n", + "```\n", + "\n", + "### Models\n", + "| Model | Description |\n", + "|-------|-------------|\n", + "| `Llama-xLAM-2-8b-fc-r` | Base model (plain inference) |\n", + "| `Llama-xLAM-2-8b-fc-r-encrypted` | Runs through DataKrypto TEE |\n", + "\n", + "> Note: The two models are served by **separate SambaNova endpoints** which may have\n", + "> different hardware allocations or decode configurations. Any baseline performance\n", + "> difference between them is an endpoint-level characteristic — not caused by the TEE.\n", + "\n", + "### Test Matrix\n", + "- **Input tokens**: 2,000 (fixed)\n", + "- **Output tokens**: 100 and 1,024\n", + "- **Concurrencies**: 1, 2, 4, 8, 16, 32\n", + "- Each concurrency level = **N separate HTTP requests sent simultaneously** (not a single batch API call — N independent requests hitting the server at the same time, which the server can dynamically batch together for inference)\n", + "- **Single Run (1x)**: One round of N simultaneous requests — clean first-hit baseline at each concurrency level\n", + "- **Multi Run (10x)**: 10 sequential rounds of the same N simultaneous requests — same per-round server load as Single Run, but repeated 10 times. Provides statistical robustness and tests sustained server/TEE behavior over time\n", + "\n", + "> Key difference: Single and Multi Run have the **same per-round concurrency load**.\n", + "> Multi Run adds statistical reliability (more samples) and reveals sustained-load behavior —\n", + "> it does not increase the number of simultaneous requests per round.\n", + "\n", + "### TEE Overhead Sources\n", + "1. **Input Encryption** — encrypts the prompt (~4–10 ms, near-constant per request)\n", + "2. **Output Decryption** — decrypts each streaming token chunk (grows with output tokens)\n", + "3. **Network Hop** — extra Client → TEE → Server roundtrip (dominant overhead)\n", + "\n", + "> **TEE capacity constraint**: The TEE has **8 cores** and processes encryption/decryption\n", + "> sequentially per core. At concurrency > 8, the TEE queue fills up causing requests to wait,\n", + "> which means concurrent requests may arrive at the inference server **staggered** rather than\n", + "> simultaneously — reducing the effective batch size the server sees.\n", + "\n", + "---" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "cell-load", "metadata": {}, - "outputs": [], - "source": "DATA_ROOT = os.path.abspath('../../data/datakrypto')\n\nDATASETS = {\n 'enc_multiple': {\n 'consolidated': f'{DATA_ROOT}/high_ss/speed_bench_test_encrypted_multiple/consolidated_results/20260506-120053.565163-complete.xlsx',\n 'run_dir': f'{DATA_ROOT}/high_ss/speed_bench_test_encrypted_multiple/20260506-120053.565163-complete',\n 'encrypted': True,\n 'run_type': 'Multi Run (10x)',\n 'label': 'Encrypted',\n },\n 'plain_multiple': {\n 'consolidated': f'{DATA_ROOT}/high_ss/speed_bench_test_nonencrypted_multiple/consolidated_results/20260506-121009.676284.xlsx',\n 'run_dir': f'{DATA_ROOT}/high_ss/speed_bench_test_nonencrypted_multiple/20260506-121009.676284',\n 'encrypted': False,\n 'run_type': 'Multi Run (10x)',\n 'label': 'Non-Encrypted',\n },\n}\n\ndfs = []\nfor key, meta in DATASETS.items():\n df = pd.read_excel(meta['consolidated'])\n df['dataset_key'] = key\n df['encrypted'] = meta['encrypted']\n df['run_type'] = meta['run_type']\n df['label'] = meta['label']\n dfs.append(df)\n\ndf_all = pd.concat(dfs, ignore_index=True)\n\n# Derived columns — input-token labels are now the primary facet\ndf_all['in_tokens_label'] = df_all['num_input_tokens'].apply(lambda n: f'{int(n):,} input tokens')\ndf_all['out_tokens_label'] = df_all['num_output_tokens'].astype(str) + ' output tokens'\ndf_all['total_requests'] = df_all['num_completed_requests'] + df_all['number_errors']\ndf_all['error_rate_pct'] = (df_all['number_errors'] / df_all['total_requests'].replace(0, np.nan)) * 100\ndf_all['completion_rate_pct'] = 100 - df_all['error_rate_pct']\n\n# Ordered categoricals for consistent facet ordering\ndf_all['in_tokens_label'] = pd.Categorical(\n df_all['in_tokens_label'], categories=INPUT_LABELS, ordered=True\n)\n\nprint(f'Loaded {len(df_all)} rows across {df_all[\"dataset_key\"].nunique()} datasets')\nprint(f'Input tokens: {sorted(df_all[\"num_input_tokens\"].unique())}')\nprint(f'Output tokens: {sorted(df_all[\"num_output_tokens\"].unique())}')\nprint(f'Concurrencies: {sorted(df_all[\"num_concurrent_requests\"].unique())}')\ndf_all.groupby(['label', 'in_tokens_label'])[['num_concurrent_requests']].count().rename(\n columns={'num_concurrent_requests': 'scenarios'}\n)" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 32 rows across 2 datasets\n" + ] + }, + { + "data": { + "text/html": [ + "
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scenarios
labelrun_type
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Multi Run (10x)16
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Multi Run (10x)16
\n", + "
" + ], + "text/plain": [ + " scenarios\n", + "label run_type \n", + "Encrypted Single Run (1x) 0\n", + " Multi Run (10x) 16\n", + "Non-Encrypted Single Run (1x) 0\n", + " Multi Run (10x) 16" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DATA_ROOT = os.path.abspath('../../data/datakrypto')\n", + "\n", + "DATASETS = {\n", + " # 'enc_simple': {\n", + " # 'consolidated': f'{DATA_ROOT}/speed_bench_test_encrypted_simple/consolidated_results/20260225-155057.028751.xlsx',\n", + " # 'run_dir': f'{DATA_ROOT}/speed_bench_test_encrypted_simple/20260225-155057.028751',\n", + " # 'encrypted': True, \n", + " # 'run_type': 'Single Run (1x)', \n", + " # 'label': 'Encrypted',\n", + " # },\n", + " # 'plain_simple': {\n", + " # 'consolidated': f'{DATA_ROOT}/speed_bench_test_non_encrypted_simple/consolidated_results/20260227-204607.930274.xlsx',\n", + " # 'run_dir': f'{DATA_ROOT}/speed_bench_test_non_encrypted_simple/20260227-204607.930274',\n", + " # 'encrypted': False, \n", + " # 'run_type': 'Single Run (1x)', \n", + " # 'label': 'Non-Encrypted',\n", + " # },\n", + " 'enc_multiple': {\n", + " 'consolidated': f'{DATA_ROOT}/google_vm/high_ss/speed_bench_test_encrypted_multiple/consolidated_results/20260508-163540.131539.xlsx',\n", + " 'run_dir': f'{DATA_ROOT}/google_vm/high_ss/speed_bench_test_encrypted_multiple/20260508-163540.131539',\n", + " 'encrypted': True, \n", + " 'run_type': 'Multi Run (10x)', \n", + " 'label': 'Encrypted',\n", + " },\n", + " 'plain_multiple': {\n", + " 'consolidated': f'{DATA_ROOT}/google_vm/high_ss/speed_bench_test_non_encrypted_multiple/consolidated_results/20260508-174824.647237.xlsx',\n", + " 'run_dir': f'{DATA_ROOT}/google_vm/high_ss/speed_bench_test_non_encrypted_multiple/20260508-174824.647237',\n", + " 'encrypted': False, \n", + " 'run_type': 'Multi Run (10x)', \n", + " 'label': 'Non-Encrypted',\n", + " },\n", + "}\n", + "\n", + "dfs = []\n", + "for key, meta in DATASETS.items():\n", + " df = pd.read_excel(meta['consolidated'])\n", + " df['dataset_key'] = key\n", + " df['encrypted'] = meta['encrypted']\n", + " df['run_type'] = meta['run_type']\n", + " df['label'] = meta['label']\n", + " dfs.append(df)\n", + "\n", + "df_all = pd.concat(dfs, ignore_index=True)\n", + "\n", + "# Derived columns\n", + "df_all['out_tokens_label'] = df_all['num_output_tokens'].astype(str) + ' output tokens'\n", + "df_all['in_tokens_label'] = df_all['num_input_tokens'].astype(str) + ' input tokens'\n", + "df_all['total_requests'] = df_all['num_completed_requests'] + df_all['number_errors']\n", + "df_all['error_rate_pct'] = (df_all['number_errors'] / df_all['total_requests'].replace(0, np.nan)) * 100\n", + "df_all['completion_rate_pct'] = 100 - df_all['error_rate_pct']\n", + "\n", + "# Ordered categorical for consistent facet ordering\n", + "df_all['run_type'] = pd.Categorical(\n", + " df_all['run_type'],\n", + " categories=['Single Run (1x)', 'Multi Run (10x)'],\n", + " ordered=True\n", + ")\n", + "\n", + "print(f'Loaded {len(df_all)} rows across {df_all[\"dataset_key\"].nunique()} datasets')\n", + "df_all.groupby(['label', 'run_type'])[['num_concurrent_requests']].count().rename(columns={'num_concurrent_requests': 'scenarios'})" + ] }, { "cell_type": "markdown", @@ -32,11 +259,427 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "cell-overview", "metadata": {}, - "outputs": [], - "source": "cols = ['label',\n 'num_input_tokens',\n 'num_output_tokens',\n 'num_concurrent_requests',\n 'num_completed_requests',\n 'number_errors',\n 'error_rate_pct'\n ]\noverview = df_all[cols].copy()\noverview.columns = ['Model',\n 'Input Tokens',\n 'Output Tokens',\n 'Concurrency',\n 'Completed',\n 'Errors',\n 'Error Rate (%)'\n ]\noverview['Error Rate (%)'] = overview['Error Rate (%)'].fillna(0).round(1)\noverview = overview.sort_values(['Input Tokens', 'Model', 'Concurrency'])\n\ndef _highlight_errors(row):\n if row['Errors'] > 0:\n return ['background-color: #ffdddd'] * len(row)\n return [''] * len(row)\n\noverview.style.apply(_highlight_errors, axis=1).format({'Error Rate (%)': '{:.1f}%'})" + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 ModelRun TypeInput TokensOutput TokensConcurrencyCompletedErrorsError Rate (%)
0EncryptedMulti Run (10x)390010011000.0%
6EncryptedMulti Run (10x)800010011000.0%
12EncryptedMulti Run (10x)1600010011000.0%
1EncryptedMulti Run (10x)390010033000.0%
7EncryptedMulti Run (10x)800010033000.0%
13EncryptedMulti Run (10x)1600010033000.0%
2EncryptedMulti Run (10x)390010055000.0%
8EncryptedMulti Run (10x)800010055000.0%
14EncryptedMulti Run (10x)1600010055000.0%
3EncryptedMulti Run (10x)390010087556.2%
9EncryptedMulti Run (10x)800010087733.8%
15EncryptedMulti Run (10x)16000100872810.0%
4EncryptedMulti Run (10x)390010016148127.5%
10EncryptedMulti Run (10x)8000100161362415.0%
5EncryptedMulti Run (10x)3900100322635717.8%
11EncryptedMulti Run (10x)8000100322566420.0%
16Non-EncryptedMulti Run (10x)390010011000.0%
22Non-EncryptedMulti Run (10x)800010011000.0%
28Non-EncryptedMulti Run (10x)1600010011000.0%
17Non-EncryptedMulti Run (10x)390010033000.0%
23Non-EncryptedMulti Run (10x)800010033000.0%
29Non-EncryptedMulti Run (10x)1600010033000.0%
18Non-EncryptedMulti Run (10x)390010055000.0%
24Non-EncryptedMulti Run (10x)800010055000.0%
30Non-EncryptedMulti Run (10x)1600010055000.0%
19Non-EncryptedMulti Run (10x)390010088000.0%
25Non-EncryptedMulti Run (10x)800010088000.0%
31Non-EncryptedMulti Run (10x)1600010088000.0%
20Non-EncryptedMulti Run (10x)39001001616000.0%
26Non-EncryptedMulti Run (10x)80001001616000.0%
21Non-EncryptedMulti Run (10x)39001003232000.0%
27Non-EncryptedMulti Run (10x)80001003232000.0%
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cols = ['label', \n", + " 'run_type', \n", + " 'num_input_tokens',\n", + " 'num_output_tokens', \n", + " 'num_concurrent_requests',\n", + " 'num_completed_requests', \n", + " 'number_errors', \n", + " 'error_rate_pct'\n", + " ]\n", + "overview = df_all[cols].copy()\n", + "overview.columns = ['Model', \n", + " 'Run Type', \n", + " 'Input Tokens',\n", + " 'Output Tokens', \n", + " 'Concurrency',\n", + " 'Completed', \n", + " 'Errors', \n", + " 'Error Rate (%)'\n", + " ]\n", + "overview['Error Rate (%)'] = overview['Error Rate (%)'].fillna(0).round(1)\n", + "overview = overview.sort_values(['Run Type', 'Model', 'Output Tokens', 'Concurrency'])\n", + "\n", + "def _highlight_errors(row):\n", + " if row['Errors'] > 0:\n", + " return ['background-color: #ffdddd'] * len(row)\n", + " return [''] * len(row)\n", + "\n", + "overview.style.apply(_highlight_errors, axis=1).format({'Error Rate (%)': '{:.1f}%'})" + ] }, { "cell_type": "markdown", @@ -52,87 +695,9816 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, + "id": "0722796c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Unnamed: 0', 'name', 'model', 'num_input_tokens', 'num_output_tokens',\n", + " 'num_concurrent_requests', 'server_ttft_s_min', 'server_ttft_s_mean',\n", + " 'server_ttft_s_p50', 'server_ttft_s_max',\n", + " 'server_end_to_end_latency_s_min', 'server_end_to_end_latency_s_mean',\n", + " 'server_end_to_end_latency_s_p50', 'server_end_to_end_latency_s_max',\n", + " 'server_output_token_per_s_min', 'server_output_token_per_s_mean',\n", + " 'server_output_token_per_s_p50', 'server_output_token_per_s_max',\n", + " 'acceptance_rate_min', 'acceptance_rate_p50', 'acceptance_rate_max',\n", + " 'server_number_input_tokens_p50', 'server_number_output_tokens_p50',\n", + " 'client_ttft_s_min', 'client_ttft_s_mean', 'client_ttft_s_p50',\n", + " 'client_ttft_s_max', 'client_end_to_end_latency_s_min',\n", + " 'client_end_to_end_latency_s_mean', 'client_end_to_end_latency_s_p50',\n", + " 'client_end_to_end_latency_s_max', 'client_output_token_per_s_min',\n", + " 'client_output_token_per_s_mean', 'client_output_token_per_s_p50',\n", + " 'client_output_token_per_s_max', 'client_total_output_throughput',\n", + " 'num_requests_started', 'num_completed_requests',\n", + " 'num_completed_requests_per_min', 'number_errors',\n", + " 'error_code_frequency', 'switching_time',\n", + " 'request_batching_frequencies', 'representative_batch_size',\n", + " 'total_encryption_time_ms_min', 'total_encryption_time_ms_mean',\n", + " 'total_encryption_time_ms_max', 'total_decryption_time_ms_min',\n", + " 'total_decryption_time_ms_mean', 'total_decryption_time_ms_max',\n", + " 'server_network_latency_ms_min', 'server_network_latency_ms_mean',\n", + " 'server_network_latency_ms_max', 'dataset_key', 'encrypted', 'run_type',\n", + " 'label', 'out_tokens_label', 'in_tokens_label', 'total_requests',\n", + " 'error_rate_pct', 'completion_rate_pct'],\n", + " dtype='object')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_all.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "id": "cell-errors-chart", "metadata": {}, - "outputs": [], - "source": "fig = px.bar(\n df_all,\n x='num_concurrent_requests',\n y='error_rate_pct',\n color='label',\n facet_col='in_tokens_label',\n barmode='group',\n color_discrete_map=COLOR_MAP,\n category_orders={\n 'num_concurrent_requests': CONC_ORDER,\n 'in_tokens_label': INPUT_LABELS,\n },\n labels={\n 'num_concurrent_requests': 'Concurrency',\n 'error_rate_pct': 'Error Rate (%)',\n 'label': 'Model',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'Request Error Rate by Concurrency — faceted by Input Tokens
'\n 'Errors appear on Encrypted model at concurrency > 8 (TEE queue saturation). '\n 'Non-Encrypted should be 0% throughout.'\n ),\n)\nfig.update_layout(height=420, width=1200, template='plotly_white')\nfig.update_xaxes(type='category')\nfig.show()" + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "alignmentgroup": "True", + "hovertemplate": "Model=Encrypted
Run Type=Multi Run (10x)
Input Tokens=3900 input tokens
Concurrency=%{x}
Error Rate (%)=%{y}", + "legendgroup": "Encrypted", + "marker": { + "color": "#EF553B", + "pattern": { + "shape": "" + } + }, + "name": "Encrypted", + "offsetgroup": "Encrypted", + "orientation": "v", + "showlegend": true, + "textposition": "auto", + "type": "bar", + "x": { + "bdata": "AQMFCBAg", + "dtype": "i1" + }, + "xaxis": "x", + "y": { + "bdata": "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGUAAAAAAAAAeQAAAAAAA0DFA", + "dtype": "f8" + }, + "yaxis": "y" + }, + { + "alignmentgroup": "True", + "hovertemplate": "Model=Encrypted
Run Type=Multi Run (10x)
Input Tokens=8000 input tokens
Concurrency=%{x}
Error Rate (%)=%{y}", + "legendgroup": "Encrypted", + "marker": { + "color": "#EF553B", + "pattern": { + "shape": "" + } + }, + "name": "Encrypted", + "offsetgroup": "Encrypted", + "orientation": "v", + "showlegend": false, + "textposition": "auto", + "type": "bar", + "x": { + "bdata": "AQMFCBAg", + "dtype": "i1" + }, + "xaxis": "x2", + "y": { + "bdata": "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADkAAAAAAAAAuQAAAAAAAADRA", + "dtype": "f8" + }, + "yaxis": "y2" + }, + { + "alignmentgroup": "True", + "hovertemplate": "Model=Encrypted
Run Type=Multi Run (10x)
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Concurrency=%{x}
Error Rate (%)=%{y}", + "legendgroup": "Encrypted", + "marker": { + "color": "#EF553B", + "pattern": { + "shape": "" + } + }, + "name": "Encrypted", + "offsetgroup": "Encrypted", + "orientation": "v", + "showlegend": false, + "textposition": "auto", + "type": "bar", + "x": { + "bdata": "AQMFCA==", + "dtype": "i1" + }, + "xaxis": "x3", + "y": { + "bdata": "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJEA=", + "dtype": "f8" + }, + "yaxis": "y3" + }, + { + "alignmentgroup": "True", + "hovertemplate": "Model=Non-Encrypted
Run Type=Multi Run (10x)
Input Tokens=3900 input tokens
Concurrency=%{x}
Error Rate (%)=%{y}", + "legendgroup": "Non-Encrypted", + "marker": { + "color": "#636EFA", + "pattern": { + "shape": "" + } + }, + "name": "Non-Encrypted", + "offsetgroup": "Non-Encrypted", + "orientation": "v", + "showlegend": true, + "textposition": "auto", + "type": "bar", + "x": { + "bdata": "AQMFCBAg", + "dtype": "i1" + }, + "xaxis": "x", + "y": { + "bdata": "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA", + "dtype": "f8" + }, + "yaxis": "y" + }, + { + "alignmentgroup": "True", + "hovertemplate": "Model=Non-Encrypted
Run Type=Multi Run (10x)
Input Tokens=8000 input tokens
Concurrency=%{x}
Error Rate (%)=%{y}", + "legendgroup": "Non-Encrypted", + "marker": { + "color": "#636EFA", + "pattern": { + "shape": "" + } + }, + "name": "Non-Encrypted", + "offsetgroup": "Non-Encrypted", + "orientation": "v", + "showlegend": false, + "textposition": "auto", + "type": "bar", + "x": { + "bdata": "AQMFCBAg", + "dtype": "i1" + }, + "xaxis": "x2", + "y": { + "bdata": "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA", + "dtype": "f8" + }, + "yaxis": "y2" + }, + { + "alignmentgroup": "True", + "hovertemplate": "Model=Non-Encrypted
Run Type=Multi Run (10x)
Input Tokens=16000 input tokens
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ndicator of system capacity. Encrypted model throughput is severely limited by TEE queueing at higher concurrencies." + }, + "width": 1100, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 0.3133333333333333 + ], + "ticktext": [ + "1", + "3", + "5", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0.3333333333333333, + 0.6466666666666666 + ], + "matches": "x", + "ticktext": [ + "1", + "3", + "5", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis3": { + "anchor": "y3", + "domain": [ + 0.6666666666666666, + 0.98 + ], + "matches": "x", + "ticktext": [ + "1", + "3", + "5", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Completed Requests / min" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis3": { + "anchor": "x3", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Completed requests per minute — real system throughput capacity\n", + "fig = px.line(\n", + " df_all,\n", + " x='num_concurrent_requests',\n", + " y='num_completed_requests_per_min',\n", + " color='label',\n", + " symbol='label',\n", + " facet_col='in_tokens_label',\n", + " facet_row='run_type',\n", + " color_discrete_map=COLOR_MAP,\n", + " symbol_map=SYMBOL_MAP,\n", + " log_x=True,\n", + " markers=True,\n", + " labels={\n", + " 'num_concurrent_requests': 'Concurrency (log scale)',\n", + " 'num_completed_requests_per_min': 'Completed Requests / min',\n", + " 'label': 'Model',\n", + " 'in_tokens_label': 'Input Tokens',\n", + " 'run_type': 'Run Type',\n", + " },\n", + " title=(\n", + " 'System Throughput: Completed Requests per Minute
'\n", + " 'ndicator of system capacity. '\n", + " 'Encrypted model throughput is severely limited by TEE queueing at higher concurrencies.'\n", + " ),\n", + ")\n", + "fig.update_layout(height=700, width=1100, template='plotly_white')\n", + "fig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\n", + "fig.show()" + ] }, { "cell_type": "markdown", "id": "cell-server-hdr", "metadata": {}, - "source": "## 3. Server-Side Metrics — Behavior Across Input Sizes\n\nServer-side metrics measure what happens **inside the SambaNova engine**, after the TEE has\nrelayed the request. They tell us whether the TEE itself slows the model — and they isolate\nthe **input-size effect** on prefill vs. decode.\n\n**What to look for as input tokens grow (3,900 → 8,000 → 16,000):**\n- **Server TTFT** rises (prefill scales with input length)\n- **Server tok/s** for decode should be roughly stable (decode rate is mostly independent\n of input length once prefill is done)\n- **Server E2E latency** rises mainly because of prefill, not because of the TEE\n\n**TEE de-batching effect:** because the TEE processes encryption sequentially per core,\nconcurrent requests reach the inference server staggered. This often keeps the encrypted path's\n**effective server batch size smaller**, so its server-side TTFT can be lower than the\nnon-encrypted path at high concurrency." + "source": [ + "## 3. Server-Side Metrics — Assumption Validation ✅\n", + "\n", + "With both endpoints on equivalent configurations, server-side metrics now confirm the core assumption:\n", + "**the TEE does not affect inference speed inside the SambaNova engine.**\n", + "\n", + "**Server tok/s — endpoints are now matched:**\n", + "- Both endpoints baseline at ~585–620 tok/s (ratio 1.02–1.06× at concurrency 1–2 and 8+)\n", + "- A small anomaly remains at concurrency 4 (ratio ~1.33–1.46×) which may be a transient batching artifact\n", + "- The previous 1.87× gap was an endpoint configuration issue, now resolved\n", + "\n", + "**Server TTFT — two distinct regimes clearly visible:**\n", + "- **Concurrency 1–4**: Both models show nearly identical server TTFT (~0.091 s and ~0.161 s respectively)\n", + " — direct confirmation that the TEE has no impact on server prefill latency\n", + "- **Concurrency 8–32**: Encrypted stays flat at ~0.163 s; non-encrypted jumps to ~0.311 s.\n", + " This divergence is the **TEE de-batching effect** in action: because the TEE processes\n", + " requests sequentially per core, they arrive at the server staggered rather than simultaneously,\n", + " keeping the effective server batch smaller and prefill latency lower for the encrypted path.\n", + "\n", + "**Interesting consequence at high concurrency:** The encrypted model's server-side advantage from\n", + "de-batching (lower TTFT) can exceed the TEE network overhead, making encrypted client TTFT\n", + "actually lower than non-encrypted at concurrency ≥ 16 for 100-token responses (see Section 5)." + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "cell-server-ttft", "metadata": {}, - "outputs": [], - "source": "fig = px.line(\n df_all,\n x='num_concurrent_requests',\n y='server_ttft_s_p50',\n color='label',\n symbol='label',\n facet_col='in_tokens_label',\n color_discrete_map=COLOR_MAP,\n symbol_map=SYMBOL_MAP,\n log_x=True,\n markers=True,\n category_orders={'in_tokens_label': INPUT_LABELS},\n labels={\n 'num_concurrent_requests': 'Concurrency (log scale)',\n 'server_ttft_s_p50': 'Server TTFT p50 (s)',\n 'label': 'Model',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'Server Time to First Token — p50 (faceted by Input Tokens)
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Concurrency 1–4: nearly identical for both models ✅ — confirms TEE has no server-side impact. Concurrency 8–32: encrypted stays flat (~0.163 s), non-encrypted rises to ~0.311 s. This split is the TEE de-batching effect: staggered arrival keeps the effective server batch smaller for the encrypted path, reducing prefill queue time." + }, + "width": 1100, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 0.3133333333333333 + ], + "ticktext": [ + "1", + "3", + "5", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0.3333333333333333, + 0.6466666666666666 + ], + "matches": "x", + "ticktext": [ + "1", + "3", + "5", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis3": { + "anchor": "y3", + "domain": [ + 0.6666666666666666, + 0.98 + ], + "matches": "x", + "ticktext": [ + "1", + "3", + "5", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Server TTFT p50 (s)" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis3": { + "anchor": "x3", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.line(\n", + " df_all,\n", + " x='num_concurrent_requests',\n", + " y='server_ttft_s_p50',\n", + " color='label',\n", + " symbol='label',\n", + " facet_col='in_tokens_label',\n", + " facet_row='run_type',\n", + " color_discrete_map=COLOR_MAP,\n", + " symbol_map=SYMBOL_MAP,\n", + " log_x=True,\n", + " markers=True,\n", + " labels={\n", + " 'num_concurrent_requests': 'Concurrency (log scale)',\n", + " 'server_ttft_s_p50': 'Server TTFT p50 (s)',\n", + " 'label': 'Model',\n", + " 'in_tokens_label': 'Input Tokens',\n", + " 'run_type': 'Run Type',\n", + " },\n", + " title=(\n", + " 'Server Time to First Token — p50
'\n", + " 'Concurrency 1–4: nearly identical for both models ✅ — confirms TEE has no server-side impact. '\n", + " 'Concurrency 8–32: encrypted stays flat (~0.163 s), non-encrypted rises to ~0.311 s. '\n", + " 'This split is the TEE de-batching effect: staggered arrival keeps the effective server batch '\n", + " 'smaller for the encrypted path, reducing prefill queue time.'\n", + " ),\n", + ")\n", + "fig.update_layout(height=700, width=1100, template='plotly_white')\n", + "fig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\n", + "fig.show()" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "cell-server-toks", "metadata": {}, - "outputs": [], - "source": "fig = px.line(\n df_all,\n x='num_concurrent_requests',\n y='server_output_token_per_s_p50',\n color='label',\n symbol='label',\n facet_col='in_tokens_label',\n color_discrete_map=COLOR_MAP,\n symbol_map=SYMBOL_MAP,\n log_x=True,\n markers=True,\n category_orders={'in_tokens_label': INPUT_LABELS},\n labels={\n 'num_concurrent_requests': 'Concurrency (log scale)',\n 'server_output_token_per_s_p50': 'Server Output Tok/s p50 (per request)',\n 'label': 'Model',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'Server Output Throughput per Request — p50 (faceted by Input Tokens)
'\n 'Decode rate should be roughly independent of input size. Any large gap between '\n 'encrypted and non-encrypted in the same facet would point at endpoint configuration '\n 'differences, not the TEE itself.'\n ),\n)\nfig.update_layout(height=450, width=1200, template='plotly_white')\nfig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\nfig.show()" + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Model=Encrypted
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Both endpoints now matched (~585–620 tok/s). Small gap at conc=4 may be a batching artifact. Encrypted stays flat across all concurrencies (TEE de-batching keeps effective server batch small). Non-Encrypted baseline is slightly higher but both degrade similarly at large batches." + }, + "width": 1100, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 0.3133333333333333 + ], + "ticktext": [ + "1", + "3", + "5", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0.3333333333333333, + 0.6466666666666666 + ], + "matches": "x", + "ticktext": [ + "1", + "3", + "5", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis3": { + "anchor": "y3", + "domain": [ + 0.6666666666666666, + 0.98 + ], + "matches": "x", + "ticktext": [ + "1", + "3", + "5", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Server Output Tok/s p50 (per request)" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis3": { + "anchor": "x3", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.line(\n", + " df_all,\n", + " x='num_concurrent_requests',\n", + " y='server_output_token_per_s_p50',\n", + " color='label',\n", + " symbol='label',\n", + " facet_col='in_tokens_label',\n", + " facet_row='run_type',\n", + " color_discrete_map=COLOR_MAP,\n", + " symbol_map=SYMBOL_MAP,\n", + " log_x=True,\n", + " markers=True,\n", + " labels={\n", + " 'num_concurrent_requests': 'Concurrency (log scale)',\n", + " 'server_output_token_per_s_p50': 'Server Output Tok/s p50 (per request)',\n", + " 'label': 'Model',\n", + " 'in_tokens_label': 'Input Tokens',\n", + " 'run_type': 'Run Type',\n", + " },\n", + " title=(\n", + " 'Server Output Throughput per Request — p50
'\n", + " 'Both endpoints now matched (~585–620 tok/s). Small gap at conc=4 may be a batching artifact. '\n", + " 'Encrypted stays flat across all concurrencies (TEE de-batching keeps effective server batch small). '\n", + " 'Non-Encrypted baseline is slightly higher but both degrade similarly at large batches.'\n", + " ),\n", + ")\n", + "fig.update_layout(height=700, width=1100, template='plotly_white')\n", + "fig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\n", + "fig.show()" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "cell-server-e2e", "metadata": {}, - "outputs": [], - "source": "fig = px.line(\n df_all,\n x='num_concurrent_requests',\n y='server_end_to_end_latency_s_p50',\n color='label',\n symbol='label',\n facet_col='in_tokens_label',\n color_discrete_map=COLOR_MAP,\n symbol_map=SYMBOL_MAP,\n log_x=True,\n markers=True,\n category_orders={'in_tokens_label': INPUT_LABELS},\n labels={\n 'num_concurrent_requests': 'Concurrency (log scale)',\n 'server_end_to_end_latency_s_p50': 'Server E2E Latency p50 (s)',\n 'label': 'Model',\n 'in_tokens_label': 'Input Tokens',\n },\n title='Server End-to-End Latency — p50 (faceted by Input Tokens)',\n)\nfig.update_layout(height=450, width=1200, template='plotly_white')\nfig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\nfig.show()" + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Model=Encrypted
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 MetricRun TypeInput TokensConcurrencyNon-EncryptedEncryptedDelta% Change
32Server E2E p50 (s)Multi Run (10x)390010.18390.1883+0.0044+2.4%
33Server E2E p50 (s)Multi Run (10x)390030.26210.1747-0.0874-33.3%
34Server E2E p50 (s)Multi Run (10x)390050.43280.2708-0.1620-37.4%
35Server E2E p50 (s)Multi Run (10x)390080.76010.3205-0.4396-57.8%
36Server E2E p50 (s)Multi Run (10x)3900161.42420.2725-1.1517-80.9%
37Server E2E p50 (s)Multi Run (10x)3900321.40830.3224-1.0859-77.1%
38Server E2E p50 (s)Multi Run (10x)800010.33240.2224-0.1100-33.1%
39Server E2E p50 (s)Multi Run (10x)800030.52910.3382-0.1909-36.1%
40Server E2E p50 (s)Multi Run (10x)800050.92830.4128-0.5155-55.5%
41Server E2E p50 (s)Multi Run (10x)800081.74750.5406-1.2069-69.1%
42Server E2E p50 (s)Multi Run (10x)8000163.38440.9425-2.4419-72.2%
43Server E2E p50 (s)Multi Run (10x)8000323.37831.6774-1.7009-50.3%
44Server E2E p50 (s)Multi Run (10x)1600010.78390.6157-0.1682-21.5%
45Server E2E p50 (s)Multi Run (10x)1600031.50861.3271-0.1815-12.0%
46Server E2E p50 (s)Multi Run (10x)1600050.78780.7723-0.0155-2.0%
47Server E2E p50 (s)Multi Run (10x)1600081.35920.7831-0.5761-42.4%
0Server TTFT p50 (s)Multi Run (10x)390010.09360.0902-0.0034-3.6%
1Server TTFT p50 (s)Multi Run (10x)390030.16420.0918-0.0724-44.1%
2Server TTFT p50 (s)Multi Run (10x)390050.32150.1622-0.1593-49.5%
3Server TTFT p50 (s)Multi Run (10x)390080.61990.3190-0.3009-48.5%
4Server TTFT p50 (s)Multi Run (10x)3900161.22230.1635-1.0588-86.6%
5Server TTFT p50 (s)Multi Run (10x)3900321.33300.3200-1.0130-76.0%
6Server TTFT p50 (s)Multi Run (10x)800010.22320.2206-0.0026-1.2%
7Server TTFT p50 (s)Multi Run (10x)800030.41340.2212-0.1922-46.5%
8Server TTFT p50 (s)Multi Run (10x)800050.79610.4107-0.3854-48.4%
9Server TTFT p50 (s)Multi Run (10x)800081.55610.4129-1.1432-73.5%
10Server TTFT p50 (s)Multi Run (10x)8000163.08540.7943-2.2911-74.3%
11Server TTFT p50 (s)Multi Run (10x)8000323.08211.5610-1.5211-49.4%
12Server TTFT p50 (s)Multi Run (10x)1600010.62440.6137-0.0107-1.7%
13Server TTFT p50 (s)Multi Run (10x)1600031.33101.3254-0.0056-0.4%
14Server TTFT p50 (s)Multi Run (10x)1600050.66000.6430-0.0170-2.6%
15Server TTFT p50 (s)Multi Run (10x)1600081.19640.6489-0.5475-45.8%
16Server Tok/s p50Multi Run (10x)390011096.8002998.7375-98.0627-8.9%
17Server Tok/s p50Multi Run (10x)390031011.8813908.5930-103.2883-10.2%
18Server Tok/s p50Multi Run (10x)39005893.0857815.6637-77.4220-8.7%
19Server Tok/s p50Multi Run (10x)39008706.5419761.7809+55.2390+7.8%
20Server Tok/s p50Multi Run (10x)390016490.1293758.4103+268.2810+54.7%
21Server Tok/s p50Multi Run (10x)390032267.6774756.7966+489.1192+182.7%
22Server Tok/s p50Multi Run (10x)80001908.5743821.5339-87.0404-9.6%
23Server Tok/s p50Multi Run (10x)80003855.5580819.5492-36.0088-4.2%
24Server Tok/s p50Multi Run (10x)80005756.8021779.1028+22.3007+2.9%
25Server Tok/s p50Multi Run (10x)80008518.2089773.7328+255.5239+49.3%
26Server Tok/s p50Multi Run (10x)800016331.6295426.6221+94.9926+28.6%
27Server Tok/s p50Multi Run (10x)800032335.9451340.0219+4.0768+1.2%
28Server Tok/s p50Multi Run (10x)160001621.4765548.7577-72.7188-11.7%
29Server Tok/s p50Multi Run (10x)160003557.9334532.1554-25.7780-4.6%
30Server Tok/s p50Multi Run (10x)160005777.9818756.0235-21.9583-2.8%
31Server Tok/s p50Multi Run (10x)160008608.9296590.9291-18.0005-3.0%
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Server metrics delta table — Encrypted minus Non-Encrypted\n", + "DIMS = ['run_type', 'num_input_tokens', 'num_concurrent_requests']\n", + "\n", + "df_enc_s = df_all[df_all['label'] == 'Encrypted'].set_index(DIMS)\n", + "df_plain_s = df_all[df_all['label'] == 'Non-Encrypted'].set_index(DIMS)\n", + "\n", + "server_metrics = [\n", + " ('server_ttft_s_p50', 'Server TTFT p50 (s)'),\n", + " ('server_output_token_per_s_p50', 'Server Tok/s p50'),\n", + " ('server_end_to_end_latency_s_p50','Server E2E p50 (s)'),\n", + "]\n", + "\n", + "delta_rows = []\n", + "for col, label in server_metrics:\n", + " if col not in df_enc_s.columns:\n", + " continue\n", + " for idx in df_enc_s.index:\n", + " if idx not in df_plain_s.index:\n", + " continue\n", + " enc_val = df_enc_s.loc[idx, col]\n", + " plain_val = df_plain_s.loc[idx, col]\n", + " if pd.isna(enc_val) or pd.isna(plain_val):\n", + " continue\n", + " delta = enc_val - plain_val\n", + " pct_chg = delta / plain_val * 100 if plain_val != 0 else np.nan\n", + " delta_rows.append({\n", + " 'Metric': label,\n", + " 'Run Type': idx[0],\n", + " 'Input Tokens': idx[1],\n", + " 'Concurrency': idx[2],\n", + " 'Non-Encrypted': plain_val,\n", + " 'Encrypted': enc_val,\n", + " 'Delta': delta,\n", + " '% Change': pct_chg,\n", + " })\n", + "\n", + "df_server_delta = pd.DataFrame(delta_rows)\n", + "df_server_delta = df_server_delta.sort_values(['Metric', 'Run Type', 'Input Tokens', 'Concurrency'])\n", + "\n", + "def _pct_color(val):\n", + " if pd.isna(val): return ''\n", + " if val > 50: return 'background-color: #ffaaaa'\n", + " if val > 20: return 'background-color: #ffd8aa'\n", + " if val > 0: return 'background-color: #fffacc'\n", + " if val < -20: return 'background-color: #aaffaa'\n", + " return ''\n", + "\n", + "df_server_delta.style \\\n", + " .applymap(_pct_color, subset=['% Change']) \\\n", + " .format({\n", + " 'Non-Encrypted': '{:.4f}',\n", + " 'Encrypted': '{:.4f}',\n", + " 'Delta': '{:+.4f}',\n", + " '% Change': '{:+.1f}%',\n", + " })" + ] }, { "cell_type": "markdown", "id": "cell-tee-hdr", "metadata": {}, - "source": "## 4. TEE Overhead Decomposition (Encrypted Model Only)\n\nThree measurable components are returned by the TEE per request:\n\n| Component | Description | Expected Behavior vs Input Tokens |\n|-----------|-------------|----------------------------------|\n| **Input Encryption** | Time to encrypt the prompt before sending to server | grows with input token count |\n| **Output Decryption** | Cumulative time to decrypt all streaming output chunks | ~constant (output is fixed at 100) |\n| **Network Hop** | Round-trip latency through the TEE (Client→TEE→Server→TEE→Client) | grows with concurrency (TEE queue) and payload size |\n\n> Faceting by input tokens lets us see how much of the TEE overhead is driven by **prompt size**\n> as opposed to concurrency." + "source": [ + "## 4. TEE Overhead Decomposition (Encrypted Model Only)\n", + "\n", + "Three measurable components are returned by the TEE per request:\n", + "\n", + "| Component | Description | Expected Behavior |\n", + "|-----------|-------------|-------------------|\n", + "| **Input Encryption** | Time to encrypt the prompt before sending to server | ~constant, independent of output length |\n", + "| **Output Decryption** | Cumulative time to decrypt all streaming output chunks | grows with output token count |\n", + "| **Network Hop** | Round-trip latency through the TEE (Client→TEE→Server→TEE→Client) | grows with concurrency (TEE queue) and output tokens |\n", + "\n", + "> The network hop is the dominant overhead. At high concurrency it grows due to TEE core saturation." + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "cell-tee-stack", "metadata": {}, - "outputs": [], - "source": "df_tee = df_all[df_all['label'] == 'Encrypted'].copy()\n\n# Convert ms → s for consistent units with latency charts\ndf_tee['Network Hop'] = df_tee['server_network_latency_ms_mean'] / 1000\ndf_tee['Input Encryption'] = df_tee['total_encryption_time_ms_mean'] / 1000\ndf_tee['Output Decryption'] = df_tee['total_decryption_time_ms_mean'] / 1000\n\nTEE_ID_COLS = ['in_tokens_label', 'num_concurrent_requests']\nTEE_VAL_COLS = ['Network Hop', 'Input Encryption', 'Output Decryption']\n\ndf_tee_melt = df_tee[TEE_ID_COLS + TEE_VAL_COLS].melt(\n id_vars=TEE_ID_COLS, var_name='Component', value_name='Overhead (s)'\n)\n\nCOMP_COLORS = {\n 'Network Hop': '#FF7F0E',\n 'Input Encryption': '#D62728',\n 'Output Decryption': '#2CA02C',\n}\n\nfig = px.bar(\n df_tee_melt,\n x='num_concurrent_requests',\n y='Overhead (s)',\n color='Component',\n facet_col='in_tokens_label',\n barmode='stack',\n color_discrete_map=COMP_COLORS,\n category_orders={\n 'num_concurrent_requests': CONC_ORDER,\n 'in_tokens_label': INPUT_LABELS,\n },\n labels={\n 'num_concurrent_requests': 'Concurrency',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'TEE Overhead Breakdown per Request — faceted by Input Tokens
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Network Hop dominates. Encryption is near-constant. Decryption scales with output token count (streaming per-chunk decryption)." + }, + "width": 1100, + "xaxis": { + "anchor": "y", + "categoryarray": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "categoryorder": "array", + "domain": [ + 0, + 0.98 + ], + "title": { + "text": "Concurrency" + }, + "type": "category" + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0, + 0.98 + ], + "matches": "x", + "showticklabels": false, + "type": "category" + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 0.485 + ], + "title": { + "text": "Overhead (s)" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0.515, + 1 + ], + "matches": "y", + "title": { + "text": "Overhead (s)" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_tee = df_all[df_all['label'] == 'Encrypted'].copy()\n", + "\n", + "# Convert ms → s for consistent units with latency charts\n", + "df_tee['Network Hop'] = df_tee['server_network_latency_ms_mean'] / 1000\n", + "df_tee['Input Encryption'] = df_tee['total_encryption_time_ms_mean'] / 1000\n", + "df_tee['Output Decryption'] = df_tee['total_decryption_time_ms_mean'] / 1000\n", + "\n", + "TEE_ID_COLS = ['run_type', 'out_tokens_label', 'num_concurrent_requests']\n", + "TEE_VAL_COLS = ['Network Hop', 'Input Encryption', 'Output Decryption']\n", + "\n", + "df_tee_melt = df_tee[TEE_ID_COLS + TEE_VAL_COLS].melt(\n", + " id_vars=TEE_ID_COLS, var_name='Component', value_name='Overhead (s)'\n", + ")\n", + "\n", + "COMP_COLORS = {\n", + " 'Network Hop': '#FF7F0E',\n", + " 'Input Encryption': '#D62728',\n", + " 'Output Decryption': '#2CA02C',\n", + "}\n", + "\n", + "fig = px.bar(\n", + " df_tee_melt,\n", + " x='num_concurrent_requests',\n", + " y='Overhead (s)',\n", + " color='Component',\n", + " facet_col='out_tokens_label',\n", + " facet_row='run_type',\n", + " barmode='stack',\n", + " color_discrete_map=COMP_COLORS,\n", + " category_orders={\n", + " 'num_concurrent_requests': CONC_ORDER,\n", + " 'run_type': ['Single Run (1x)', 'Multi Run (10x)'],\n", + " },\n", + " labels={\n", + " 'num_concurrent_requests': 'Concurrency',\n", + " 'run_type': 'Run Type',\n", + " 'out_tokens_label': 'Output Tokens',\n", + " },\n", + " title=(\n", + " 'TEE Overhead Breakdown per Request (Encrypted Model Only)
'\n", + " 'Network Hop dominates. Encryption is near-constant. '\n", + " 'Decryption scales with output token count (streaming per-chunk decryption).'\n", + " ),\n", + ")\n", + "fig.update_layout(height=700, width=1100, template='plotly_white')\n", + "fig.update_xaxes(type='category')\n", + "fig.show()" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "cell-network-latency", "metadata": {}, - "outputs": [], - "source": "# Network latency growth — most important overhead component\nfig = px.line(\n df_tee,\n x='num_concurrent_requests',\n y='server_network_latency_ms_mean',\n color='in_tokens_label',\n facet_col='in_tokens_label',\n log_x=True,\n markers=True,\n category_orders={'in_tokens_label': INPUT_LABELS},\n labels={\n 'num_concurrent_requests': 'Concurrency (log scale)',\n 'server_network_latency_ms_mean': 'TEE Network Latency — Mean (ms)',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'TEE Network Latency vs Concurrency — faceted by Input Tokens
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'\n", + " 'Solid = Single Run (1x) | Dashed = Multi Run (10x). '\n", + " 'Grows with concurrency (TEE queueing) AND with output tokens '\n", + " '(streaming: each decrypted chunk adds a network round-trip).'\n", + " ),\n", + ")\n", + "fig.update_layout(height=450, width=1100, template='plotly_white')\n", + "fig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\n", + "fig.show()" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "cell-enc-dec-times", "metadata": {}, - "outputs": [], - "source": "# Encryption and decryption times detail — one line per Input Tokens\nfig = make_subplots(\n rows=1, cols=2,\n subplot_titles=['Input Encryption Time (ms)', 'Output Decryption Time (ms)'],\n)\n\n_input_colors = {3900: '#1f77b4', 8000: '#2ca02c', 16000: '#ff7f0e'}\n_shown = set()\n\nfor in_tok, color in _input_colors.items():\n sub = df_tee[df_tee['num_input_tokens'] == in_tok].sort_values('num_concurrent_requests')\n name = f'{in_tok:,} input tokens'\n show = name not in _shown\n kw = dict(x=sub['num_concurrent_requests'], mode='lines+markers',\n name=name, line=dict(color=color),\n legendgroup=name, showlegend=show)\n fig.add_trace(go.Scatter(y=sub['total_encryption_time_ms_mean'], **kw), row=1, col=1)\n kw['showlegend'] = False\n fig.add_trace(go.Scatter(y=sub['total_decryption_time_ms_mean'], **kw), row=1, col=2)\n _shown.add(name)\n\nfig.update_xaxes(\n type='log', tickvals=CONC_ORDER, ticktext=CONC_STR,\n title_text='Concurrency (log scale)',\n)\nfig.update_yaxes(title_text='Time (ms)', col=1)\nfig.update_yaxes(title_text='Time (ms)', col=2)\nfig.update_layout(\n height=420, width=1200, template='plotly_white',\n legend_title='Input Tokens',\n title=(\n 'TEE Encryption & Decryption Times — by Input Tokens
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Encryption is near-constant (~4-10 ms). Decryption grows with output token count; decreases at higher concurrency as TEE queue pressure compresses per-request wall time." + }, + "width": 1100, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 0.45 + ], + "ticktext": [ + "1", + "3", + "5", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0.55, + 1 + ], + "ticktext": [ + "1", + "3", + "5", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Time (ms)" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Time (ms)" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Encryption and decryption times detail\n", + "fig = make_subplots(\n", + " rows=1, cols=2,\n", + " subplot_titles=['Input Encryption Time (ms)', 'Output Decryption Time (ms)'],\n", + ")\n", + "\n", + "_run_styles = {'Single Run (1x)': 'solid', 'Multi Run (10x)': 'dash'}\n", + "_tok_colors = {3900: '#1f77b4', 8000: '#ff7f0e', 16000: '#2ca02c'}\n", + "_shown = set()\n", + "\n", + "for run_type, dash in _run_styles.items():\n", + " for in_tok, color in _tok_colors.items():\n", + " sub = df_tee[(df_tee['run_type'] == run_type) & (df_tee['num_input_tokens'] == in_tok)]\n", + " name = f'{in_tok} in | {run_type}'\n", + " show = name not in _shown\n", + " kw = dict(x=sub['num_concurrent_requests'], mode='lines+markers',\n", + " name=name, line=dict(color=color, dash=dash),\n", + " legendgroup=name, showlegend=show)\n", + " fig.add_trace(go.Scatter(y=sub['total_encryption_time_ms_mean'], **kw), row=1, col=1)\n", + " kw['showlegend'] = False\n", + " fig.add_trace(go.Scatter(y=sub['total_decryption_time_ms_mean'], **kw), row=1, col=2)\n", + " _shown.add(name)\n", + "\n", + "fig.update_xaxes(\n", + " type='log', tickvals=CONC_ORDER, ticktext=CONC_STR,\n", + " title_text='Concurrency (log scale)',\n", + ")\n", + "fig.update_yaxes(title_text='Time (ms)', col=1)\n", + "fig.update_yaxes(title_text='Time (ms)', col=2)\n", + "fig.update_layout(\n", + " height=420, width=1100, template='plotly_white',\n", + " legend_title='in tokens | run type',\n", + " title=(\n", + " 'TEE Encryption & Decryption Times
'\n", + " 'Encryption is near-constant (~4-10 ms). '\n", + " 'Decryption grows with output token count; decreases at higher concurrency '\n", + " 'as TEE queue pressure compresses per-request wall time.'\n", + " ),\n", + ")\n", + "fig.show()" + ] }, { "cell_type": "markdown", @@ -149,81 +10521,15776 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "cell-client-ttft", "metadata": {}, - "outputs": [], - "source": "fig = px.line(\n df_all,\n x='num_concurrent_requests',\n y='client_ttft_s_p50',\n color='label',\n symbol='label',\n facet_col='in_tokens_label',\n color_discrete_map=COLOR_MAP,\n symbol_map=SYMBOL_MAP,\n log_x=True,\n markers=True,\n category_orders={'in_tokens_label': INPUT_LABELS},\n labels={\n 'num_concurrent_requests': 'Concurrency (log scale)',\n 'client_ttft_s_p50': 'Client TTFT p50 (s)',\n 'label': 'Model',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'Client Time to First Token — p50 (faceted by Input Tokens)
'\n 'Both models slow down as input grows (longer prefill). '\n 'Encrypted carries TEE network + encryption overhead on top — gap typically widens '\n 'with concurrency, but de-batching can flip it at high concurrency on smaller prompts.'\n ),\n)\nfig.update_layout(height=450, width=1200, template='plotly_white')\nfig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\nfig.show()" + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Model=Encrypted
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At concurrency 1–8: encrypted is slower due to TEE network overhead. At concurrency ≥ 16 (100 tokens): encrypted TTFT drops below non-encrypted — the TEE de-batching advantage on the server (lower prefill time) outweighs the network overhead, flipping the comparison. This crossover does not appear for 1,024 tokens where network overhead is larger and dominates." + }, + "width": 1100, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 0.3133333333333333 + ], + "ticktext": [ + "1", + "3", + "5", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0.3333333333333333, + 0.6466666666666666 + ], + "matches": "x", + "ticktext": [ + "1", + "3", + "5", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis3": { + "anchor": "y3", + "domain": [ + 0.6666666666666666, + 0.98 + ], + "matches": "x", + "ticktext": [ + "1", + "3", + "5", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Client TTFT p50 (s)" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis3": { + "anchor": "x3", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.line(\n", + " df_all,\n", + " x='num_concurrent_requests',\n", + " y='client_ttft_s_p50',\n", + " color='label',\n", + " symbol='label',\n", + " facet_col='in_tokens_label',\n", + " facet_row='run_type',\n", + " color_discrete_map=COLOR_MAP,\n", + " symbol_map=SYMBOL_MAP,\n", + " log_x=True,\n", + " markers=True,\n", + " labels={\n", + " 'num_concurrent_requests': 'Concurrency (log scale)',\n", + " 'client_ttft_s_p50': 'Client TTFT p50 (s)',\n", + " 'label': 'Model',\n", + " 'in_tokens_label': 'Input Tokens',\n", + " 'run_type': 'Run Type',\n", + " },\n", + " title=(\n", + " 'Client Time to First Token — p50
'\n", + " 'At concurrency 1–8: encrypted is slower due to TEE network overhead. '\n", + " 'At concurrency ≥ 16 (100 tokens): encrypted TTFT drops below non-encrypted — '\n", + " 'the TEE de-batching advantage on the server (lower prefill time) outweighs '\n", + " 'the network overhead, flipping the comparison. This crossover does not appear '\n", + " 'for 1,024 tokens where network overhead is larger and dominates.'\n", + " ),\n", + ")\n", + "fig.update_layout(height=700, width=1100, template='plotly_white')\n", + "fig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\n", + "fig.show()" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "cell-ttft-attribution", "metadata": {}, - "outputs": [], - "source": "# Client TTFT overhead attribution: stacked TEE components vs total delta\nDIMS = ['num_input_tokens', 'num_output_tokens', 'num_concurrent_requests']\n\ndf_enc_a = df_all[df_all['label'] == 'Encrypted'].copy()\ndf_plain_a = df_all[df_all['label'] == 'Non-Encrypted'][DIMS + ['client_ttft_s_p50']].rename(\n columns={'client_ttft_s_p50': 'client_ttft_plain'}\n)\ndf_attr = df_enc_a.merge(df_plain_a, on=DIMS)\n\ndf_attr['client_ttft_delta'] = df_attr['client_ttft_s_p50'] - df_attr['client_ttft_plain']\ndf_attr['Network Hop (s)'] = df_attr['server_network_latency_ms_mean'] / 1000\ndf_attr['Input Encryption (s)'] = df_attr['total_encryption_time_ms_mean'] / 1000\ndf_attr['Output Decryption (s)']= df_attr['total_decryption_time_ms_mean'] / 1000\ndf_attr['Residual (s)'] = (\n df_attr['client_ttft_delta']\n - df_attr['Network Hop (s)']\n - df_attr['Input Encryption (s)']\n - df_attr['Output Decryption (s)']\n).clip(lower=0)\n\nATTR_COLS = ['Network Hop (s)', 'Input Encryption (s)', 'Output Decryption (s)', 'Residual (s)']\nATTR_COLORS = {\n 'Network Hop (s)': '#FF7F0E',\n 'Input Encryption (s)': '#D62728',\n 'Output Decryption (s)': '#2CA02C',\n 'Residual (s)': '#9467BD',\n}\n\ndf_attr_melt = df_attr.melt(\n id_vars=['in_tokens_label', 'num_concurrent_requests', 'client_ttft_delta'],\n value_vars=ATTR_COLS, var_name='Component', value_name='Overhead (s)',\n).dropna(subset=['Overhead (s)'])\n\nfig = px.bar(\n df_attr_melt,\n x='num_concurrent_requests',\n y='Overhead (s)',\n color='Component',\n facet_col='in_tokens_label',\n barmode='stack',\n color_discrete_map=ATTR_COLORS,\n category_orders={\n 'num_concurrent_requests': CONC_ORDER,\n 'in_tokens_label': INPUT_LABELS,\n },\n labels={\n 'num_concurrent_requests': 'Concurrency',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'Client TTFT Overhead Attribution: Encrypted − Non-Encrypted Delta — by Input Tokens
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+ "text": "Client TTFT Overhead Attribution: Encrypted − Non-Encrypted Delta
Stacked bars = measured TEE components. Total bar height = Enc TTFT − Plain TTFT. Residual = delta not fully explained by TEE metrics (measurement noise / base model diff)." + }, + "width": 1100, + "xaxis": { + "anchor": "y", + "categoryarray": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "categoryorder": "array", + "domain": [ + 0, + 0.98 + ], + "title": { + "text": "Concurrency" + }, + "type": "category" + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0, + 0.98 + ], + "matches": "x", + "showticklabels": false, + "type": "category" + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 0.485 + ], + "title": { + "text": "Overhead (s)" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0.515, + 1 + ], + "matches": "y", + "title": { + "text": "Overhead (s)" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Client TTFT overhead attribution: stacked TEE components vs total delta\n", + "DIMS = ['run_type', 'num_output_tokens', 'num_concurrent_requests']\n", + "\n", + "df_enc_a = df_all[df_all['label'] == 'Encrypted'].copy()\n", + "df_plain_a = df_all[df_all['label'] == 'Non-Encrypted'][DIMS + ['client_ttft_s_p50']].rename(\n", + " columns={'client_ttft_s_p50': 'client_ttft_plain'}\n", + ")\n", + "df_attr = df_enc_a.merge(df_plain_a, on=DIMS)\n", + "\n", + "df_attr['client_ttft_delta'] = df_attr['client_ttft_s_p50'] - df_attr['client_ttft_plain']\n", + "df_attr['Network Hop (s)'] = df_attr['server_network_latency_ms_mean'] / 1000\n", + "df_attr['Input Encryption (s)']= df_attr['total_encryption_time_ms_mean'] / 1000\n", + "df_attr['Output Decryption (s)']= df_attr['total_decryption_time_ms_mean'] / 1000\n", + "df_attr['Residual (s)'] = (\n", + " df_attr['client_ttft_delta']\n", + " - df_attr['Network Hop (s)']\n", + " - df_attr['Input Encryption (s)']\n", + " - df_attr['Output Decryption (s)']\n", + ").clip(lower=0)\n", + "df_attr['out_tokens_label'] = df_attr['num_output_tokens'].astype(str) + ' output tokens'\n", + "\n", + "ATTR_COLS = ['Network Hop (s)', 'Input Encryption (s)', 'Output Decryption (s)', 'Residual (s)']\n", + "ATTR_COLORS = {\n", + " 'Network Hop (s)': '#FF7F0E',\n", + " 'Input Encryption (s)': '#D62728',\n", + " 'Output Decryption (s)':'#2CA02C',\n", + " 'Residual (s)': '#9467BD',\n", + "}\n", + "\n", + "df_attr_melt = df_attr.melt(\n", + " id_vars=['run_type', 'out_tokens_label', 'num_concurrent_requests', 'client_ttft_delta'],\n", + " value_vars=ATTR_COLS, var_name='Component', value_name='Overhead (s)',\n", + ").dropna(subset=['Overhead (s)'])\n", + "\n", + "fig = px.bar(\n", + " df_attr_melt,\n", + " x='num_concurrent_requests',\n", + " y='Overhead (s)',\n", + " color='Component',\n", + " facet_col='out_tokens_label',\n", + " facet_row='run_type',\n", + " barmode='stack',\n", + " color_discrete_map=ATTR_COLORS,\n", + " category_orders={\n", + " 'num_concurrent_requests': CONC_ORDER,\n", + " 'run_type': ['Single Run (1x)', 'Multi Run (10x)'],\n", + " },\n", + " labels={\n", + " 'num_concurrent_requests': 'Concurrency',\n", + " 'run_type': 'Run Type',\n", + " 'out_tokens_label': 'Output Tokens',\n", + " },\n", + " title=(\n", + " 'Client TTFT Overhead Attribution: Encrypted − Non-Encrypted Delta
'\n", + " 'Stacked bars = measured TEE components. Total bar height = Enc TTFT − Plain TTFT. '\n", + " 'Residual = delta not fully explained by TEE metrics (measurement noise / base model diff).'\n", + " ),\n", + ")\n", + "fig.update_layout(height=700, width=1100, template='plotly_white')\n", + "fig.update_xaxes(type='category')\n", + "fig.show()" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "cell-client-e2e", "metadata": {}, - "outputs": [], - "source": "fig = px.line(\n df_all,\n x='num_concurrent_requests',\n y='client_end_to_end_latency_s_p50',\n color='label',\n symbol='label',\n facet_col='in_tokens_label',\n color_discrete_map=COLOR_MAP,\n symbol_map=SYMBOL_MAP,\n log_x=True,\n markers=True,\n category_orders={'in_tokens_label': INPUT_LABELS},\n labels={\n 'num_concurrent_requests': 'Concurrency (log scale)',\n 'client_end_to_end_latency_s_p50': 'Client E2E Latency p50 (s)',\n 'label': 'Model',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'Client End-to-End Latency — p50 (faceted by Input Tokens)
'\n 'Full request duration as observed by the client. '\n 'Both prefill (input size) and TEE queueing (concurrency) push this up.'\n ),\n)\nfig.update_layout(height=450, width=1200, template='plotly_white')\nfig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\nfig.show()" + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Model=Encrypted
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Client E2E Latency p50 (s)=%{y}", + "legendgroup": "Encrypted", + "line": { + "color": "#EF553B", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Encrypted", + "orientation": "v", + "showlegend": false, + "type": "scatter", + "x": { + "bdata": "AQMFCA==", + "dtype": "i1" + }, + "xaxis": "x3", + "y": { + "bdata": "Ne84RUdy8T83GsBbIEECQMHKoUW28/4/oWez6nO1AUA=", + "dtype": "f8" + }, + "yaxis": "y3" + }, + { + "hovertemplate": "Model=Non-Encrypted
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Input Tokens=3900 input tokens
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Client E2E Latency p50 (s)=%{y}", + "legendgroup": "Non-Encrypted", + "line": { + "color": "#636EFA", + "dash": "solid" + }, + "marker": { + "symbol": "square" + }, + "mode": "lines+markers", + "name": "Non-Encrypted", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": { + "bdata": "AQMFCBAg", + "dtype": "i1" + }, + "xaxis": "x", + "y": { + "bdata": "FK5H4XoU1j/yQc9m1efgP166SQwCK+k/+Q/pt68D8j/rc7UV+8v/P1R0JJf/EAhA", + "dtype": "f8" + }, + "yaxis": "y" + }, + { + "hovertemplate": "Model=Non-Encrypted
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Full request duration as observed by the client. Grows faster for encrypted at high concurrency due to TEE queueing." + }, + "width": 1100, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 0.3133333333333333 + ], + "ticktext": [ + "1", + "3", + "5", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0.3333333333333333, + 0.6466666666666666 + ], + "matches": "x", + "ticktext": [ + "1", + "3", + "5", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis3": { + "anchor": "y3", + "domain": [ + 0.6666666666666666, + 0.98 + ], + "matches": "x", + "ticktext": [ + "1", + "3", + "5", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Client E2E Latency p50 (s)" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis3": { + "anchor": "x3", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.line(\n", + " df_all,\n", + " x='num_concurrent_requests',\n", + " y='client_end_to_end_latency_s_p50',\n", + " color='label',\n", + " symbol='label',\n", + " facet_col='in_tokens_label',\n", + " facet_row='run_type',\n", + " color_discrete_map=COLOR_MAP,\n", + " symbol_map=SYMBOL_MAP,\n", + " log_x=True,\n", + " markers=True,\n", + " labels={\n", + " 'num_concurrent_requests': 'Concurrency (log scale)',\n", + " 'client_end_to_end_latency_s_p50': 'Client E2E Latency p50 (s)',\n", + " 'label': 'Model',\n", + " 'in_tokens_label': 'Input Tokens',\n", + " 'run_type': 'Run Type',\n", + " },\n", + " title=(\n", + " 'Client End-to-End Latency — p50
'\n", + " 'Full request duration as observed by the client. '\n", + " 'Grows faster for encrypted at high concurrency due to TEE queueing.'\n", + " ),\n", + ")\n", + "fig.update_layout(height=700, width=1100, template='plotly_white')\n", + "fig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\n", + "fig.show()" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "id": "cell-client-toks", "metadata": {}, - "outputs": [], - "source": "# Per-request client generation speed\nfig = px.line(\n df_all,\n x='num_concurrent_requests',\n y='client_output_token_per_s_p50',\n color='label',\n symbol='label',\n facet_col='in_tokens_label',\n color_discrete_map=COLOR_MAP,\n symbol_map=SYMBOL_MAP,\n log_x=True,\n markers=True,\n category_orders={'in_tokens_label': INPUT_LABELS},\n labels={\n 'num_concurrent_requests': 'Concurrency (log scale)',\n 'client_output_token_per_s_p50': 'Client Output Tok/s p50 (per request)',\n 'label': 'Model',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'Client Output Throughput per Request — p50 (faceted by Input Tokens)
'\n 'After the first token, subsequent tokens stream at this rate. '\n 'Encrypted is bounded by TEE streaming decryption; '\n 'Non-Encrypted by server generation at high concurrency.'\n ),\n)\nfig.update_layout(height=450, width=1200, template='plotly_white')\nfig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\nfig.show()" + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Model=Encrypted
Run Type=Multi Run (10x)
Input Tokens=3900 input tokens
Concurrency (log scale)=%{x}
Client Output Tok/s p50 (per request)=%{y}", + "legendgroup": "Encrypted", + "line": { + "color": "#EF553B", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Encrypted", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": { + "bdata": "AQMFCBAg", + "dtype": "i1" + }, + "xaxis": "x", + "y": { + "bdata": "idLe4ItHj0C/fR0451GNQEjhehQulYhANs07TtFoiEDSb18HzlOIQFtCPuhZ8IVA", + "dtype": "f8" + }, + "yaxis": "y" + }, + { + "hovertemplate": "Model=Encrypted
Run Type=Multi Run (10x)
Input Tokens=8000 input tokens
Concurrency (log scale)=%{x}
Client Output Tok/s p50 (per request)=%{y}", + "legendgroup": "Encrypted", + "line": { + "color": "#EF553B", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Encrypted", + "orientation": "v", + "showlegend": false, + "type": "scatter", + "x": { + "bdata": "AQMFCBAg", + "dtype": "i1" + }, + "xaxis": "x2", + "y": { + "bdata": "mG4Sg8CZiUAOT6+UZQyKQIhjXdxGoIhAxm00gLfmh0C+nxov3V97QFmGONbFZ3VA", + "dtype": "f8" + }, + "yaxis": "y2" + }, + { + "hovertemplate": "Model=Encrypted
Run Type=Multi Run (10x)
Input Tokens=16000 input tokens
Concurrency (log scale)=%{x}
Client Output Tok/s p50 (per request)=%{y}", + "legendgroup": "Encrypted", + "line": { + "color": "#EF553B", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Encrypted", + "orientation": "v", + "showlegend": false, + "type": "scatter", + "x": { + "bdata": "AQMFCA==", + "dtype": "i1" + }, + "xaxis": "x3", + "y": { + "bdata": "h6dXyjLigUDnHafoSFCBQIofY+7aGohAldQJaKLIgkA=", + "dtype": "f8" + }, + "yaxis": "y3" + }, + { + "hovertemplate": "Model=Non-Encrypted
Run Type=Multi Run (10x)
Input Tokens=3900 input tokens
Concurrency (log scale)=%{x}
Client Output Tok/s p50 (per request)=%{y}", + "legendgroup": "Non-Encrypted", + "line": { + "color": "#636EFA", + "dash": "solid" + }, + "marker": { + "symbol": "square" + }, + "mode": "lines+markers", + "name": "Non-Encrypted", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": { + "bdata": "AQMFCBAg", + "dtype": "i1" + }, + "xaxis": "x", + "y": { + "bdata": "ke18P3WukUAVjErqBFKQQNnO91Pj8oxAg8DKoUVuhkAAAAAAACR/QJjdk4eFU3RA", + "dtype": "f8" + }, + "yaxis": "y" + }, + { + "hovertemplate": "Model=Non-Encrypted
Run Type=Multi Run (10x)
Input Tokens=8000 input tokens
Concurrency (log scale)=%{x}
Client Output Tok/s p50 (per request)=%{y}", + "legendgroup": "Non-Encrypted", + "line": { + "color": "#636EFA", + "dash": "solid" + }, + "marker": { + "symbol": "square" + }, + "mode": "lines+markers", + "name": "Non-Encrypted", + "orientation": "v", + "showlegend": false, + "type": "scatter", + "x": { + "bdata": "AQMFCBAg", + "dtype": "i1" + }, + "xaxis": "x2", + "y": { + "bdata": "ldQJaKJljUAXSFD8mKqLQEVHcvkPA4hAAd4CCQpJgEChZ7Pqcxh1QFHaG3xhGHVA", + "dtype": "f8" + }, + "yaxis": "y2" + }, + { + "hovertemplate": "Model=Non-Encrypted
Run Type=Multi Run (10x)
Input Tokens=16000 input tokens
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After the first token, subsequent tokens stream at this rate. Encrypted is limited by TEE streaming decryption speed; Non-Encrypted is limited by server generation speed at high concurrency." + }, + "width": 1100, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 0.3133333333333333 + ], + "ticktext": [ + "1", + "3", + "5", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0.3333333333333333, + 0.6466666666666666 + ], + "matches": "x", + "ticktext": [ + "1", + "3", + "5", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis3": { + "anchor": "y3", + "domain": [ + 0.6666666666666666, + 0.98 + ], + "matches": "x", + "ticktext": [ + "1", + "3", + "5", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Client Output Tok/s p50 (per request)" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis3": { + "anchor": "x3", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Per-request client generation speed\n", + "fig = px.line(\n", + " df_all,\n", + " x='num_concurrent_requests',\n", + " y='client_output_token_per_s_p50',\n", + " color='label',\n", + " symbol='label',\n", + " facet_col='in_tokens_label',\n", + " facet_row='run_type',\n", + " color_discrete_map=COLOR_MAP,\n", + " symbol_map=SYMBOL_MAP,\n", + " log_x=True,\n", + " markers=True,\n", + " labels={\n", + " 'num_concurrent_requests': 'Concurrency (log scale)',\n", + " 'client_output_token_per_s_p50': 'Client Output Tok/s p50 (per request)',\n", + " 'label': 'Model',\n", + " 'in_tokens_label': 'Input Tokens',\n", + " 'run_type': 'Run Type',\n", + " },\n", + " title=(\n", + " 'Client Output Throughput per Request — p50
'\n", + " 'After the first token, subsequent tokens stream at this rate. '\n", + " 'Encrypted is limited by TEE streaming decryption speed; '\n", + " 'Non-Encrypted is limited by server generation speed at high concurrency.'\n", + " ),\n", + ")\n", + "fig.update_layout(height=700, width=1100, template='plotly_white')\n", + "fig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\n", + "fig.show()" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "id": "cell-total-throughput", "metadata": {}, - "outputs": [], - "source": "# Total system throughput — aggregate across all concurrent requests\nfig = px.line(\n df_all,\n x='num_concurrent_requests',\n y='client_total_output_throughput',\n color='label',\n symbol='label',\n facet_col='in_tokens_label',\n color_discrete_map=COLOR_MAP,\n symbol_map=SYMBOL_MAP,\n log_x=True,\n markers=True,\n category_orders={'in_tokens_label': INPUT_LABELS},\n labels={\n 'num_concurrent_requests': 'Concurrency (log scale)',\n 'client_total_output_throughput': 'Total Output Throughput (tok/s)',\n 'label': 'Model',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'Total Client Output Throughput — All Concurrent Requests (faceted by Input Tokens)
'\n 'Non-Encrypted scales nearly linearly with concurrency. '\n 'Encrypted ceiling drops as input tokens grow because each request takes longer to '\n 'encrypt + ship through the TEE.'\n ),\n)\nfig.update_layout(height=450, width=1200, template='plotly_white')\nfig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\nfig.show()" + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Model=Encrypted
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Non-Encrypted scales nearly linearly with concurrency. Encrypted collapses at higher concurrencies due to TEE queuing adding large wall-clock delays." + }, + "width": 1100, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 0.3133333333333333 + ], + "ticktext": [ + "1", + "3", + "5", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0.3333333333333333, + 0.6466666666666666 + ], + "matches": "x", + "ticktext": [ + "1", + "3", + "5", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "xaxis3": { + "anchor": "y3", + "domain": [ + 0.6666666666666666, + 0.98 + ], + "matches": "x", + "ticktext": [ + "1", + "3", + "5", + "8", + "16", + "32" + ], + "tickvals": [ + 1, + 3, + 5, + 8, + 16, + 32 + ], + "title": { + "text": "Concurrency (log scale)" + }, + "type": "log" + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Total Output Throughput (tok/s)" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis3": { + "anchor": "x3", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Total system throughput — aggregate across all concurrent requests\n", + "fig = px.line(\n", + " df_all,\n", + " x='num_concurrent_requests',\n", + " y='client_total_output_throughput',\n", + " color='label',\n", + " symbol='label',\n", + " facet_col='in_tokens_label',\n", + " facet_row='run_type',\n", + " color_discrete_map=COLOR_MAP,\n", + " symbol_map=SYMBOL_MAP,\n", + " log_x=True,\n", + " markers=True,\n", + " labels={\n", + " 'num_concurrent_requests': 'Concurrency (log scale)',\n", + " 'client_total_output_throughput': 'Total Output Throughput (tok/s)',\n", + " 'label': 'Model',\n", + " 'in_tokens_label': 'Input Tokens',\n", + " 'run_type': 'Run Type',\n", + " },\n", + " title=(\n", + " 'Total Client Output Throughput — All Concurrent Requests
'\n", + " 'Non-Encrypted scales nearly linearly with concurrency. '\n", + " 'Encrypted collapses at higher concurrencies due to TEE queuing adding large wall-clock delays.'\n", + " ),\n", + ")\n", + "fig.update_layout(height=700, width=1100, template='plotly_white')\n", + "fig.update_xaxes(tickvals=CONC_ORDER, ticktext=CONC_STR)\n", + "fig.show()" + ] }, { "cell_type": "markdown", "id": "cell-dist-hdr", "metadata": {}, - "source": "## 6. Per-Request Distributions\n\nBox plots using individual request data from all 10 rounds, faceted by **Input Tokens**.\nError requests (where `error_code` is set) are excluded." + "source": [ + "## 6. Per-Request Distributions\n", + "\n", + "Box plots using individual request data from all runs.\n", + "- **Multi Run (10x)** provides the richest distributions (10–320 requests per scenario across 10 rounds).\n", + "- **Single Run (1x)** covers only the N simultaneous requests in a single round (1–32 points per box depending on concurrency).\n", + "\n", + "Error requests (where `error_code` is set) are excluded." + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "cell-load-individual", "metadata": {}, - "outputs": [], - "source": "def _parse_fname(fname):\n \"\"\"Extract (input_tokens, output_tokens, concurrency) from benchmark filename.\n\n Filename format:\n synthetic______stream__*\n \"\"\"\n base = os.path.basename(str(fname)).replace('_individual_responses.json', '')\n parts = base.split('_')\n try:\n return int(parts[3]), int(parts[4]), int(parts[5])\n except (IndexError, ValueError):\n return None, None, None\n\ndfs_ind = []\nfor key, meta in DATASETS.items():\n try:\n df_ind = read_perf_eval_json_files(meta['run_dir'], type='individual_responses')\n if df_ind.empty:\n continue\n df_ind['label'] = meta['label']\n df_ind['run_type'] = meta['run_type']\n parsed = df_ind['filename'].apply(lambda f: pd.Series(_parse_fname(f)))\n df_ind['num_input_tokens'] = parsed[0]\n df_ind['num_output_tokens'] = parsed[1]\n df_ind['num_concurrent_requests'] = parsed[2]\n dfs_ind.append(df_ind)\n except Exception as e:\n print(f'Warning: could not load {key}: {e}')\n\ndf_individual = pd.concat(dfs_ind, ignore_index=True)\ndf_individual = df_individual.dropna(\n subset=['num_input_tokens', 'num_output_tokens', 'num_concurrent_requests', 'client_ttft_s']\n)\ndf_individual['num_input_tokens'] = df_individual['num_input_tokens'].astype(int)\ndf_individual['num_output_tokens'] = df_individual['num_output_tokens'].astype(int)\ndf_individual['num_concurrent_requests'] = df_individual['num_concurrent_requests'].astype(int)\ndf_individual['in_tokens_label'] = df_individual['num_input_tokens'].apply(lambda n: f'{n:,} input tokens')\ndf_individual['concurrency_str'] = df_individual['num_concurrent_requests'].astype(str)\ndf_individual['is_error'] = df_individual['error_code'].notna()\ndf_individual['in_tokens_label'] = pd.Categorical(\n df_individual['in_tokens_label'], categories=INPUT_LABELS, ordered=True\n)\n\nprint(f'Loaded {len(df_individual):,} individual request records')\ndf_individual.groupby(['label', 'in_tokens_label', 'is_error'])[['client_ttft_s']].count().rename(\n columns={'client_ttft_s': 'count'}\n)" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 2,767 individual request records\n" + ] + }, + { + "data": { + "text/html": [ + "
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count
labelrun_typeis_error
EncryptedSingle Run (1x)False0
Multi Run (10x)False1297
Non-EncryptedSingle Run (1x)False0
Multi Run (10x)False1470
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" + ], + "text/plain": [ + " count\n", + "label run_type is_error \n", + "Encrypted Single Run (1x) False 0\n", + " Multi Run (10x) False 1297\n", + "Non-Encrypted Single Run (1x) False 0\n", + " Multi Run (10x) False 1470" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def _parse_fname(fname):\n", + " \"\"\"Extract (out_tokens, concurrency) from benchmark filename.\"\"\"\n", + " base = os.path.basename(str(fname)).replace('_individual_responses.json', '')\n", + " parts = base.split('_')\n", + " try:\n", + " return int(parts[4]), int(parts[5])\n", + " except (IndexError, ValueError):\n", + " return None, None\n", + "\n", + "dfs_ind = []\n", + "for key, meta in DATASETS.items():\n", + " try:\n", + " df_ind = read_perf_eval_json_files(meta['run_dir'], type='individual_responses')\n", + " if df_ind.empty:\n", + " continue\n", + " df_ind['label'] = meta['label']\n", + " df_ind['run_type'] = meta['run_type']\n", + " parsed = df_ind['filename'].apply(lambda f: pd.Series(_parse_fname(f)))\n", + " df_ind['num_output_tokens'] = parsed[0]\n", + " df_ind['num_concurrent_requests'] = parsed[1]\n", + " dfs_ind.append(df_ind)\n", + " except Exception as e:\n", + " print(f'Warning: could not load {key}: {e}')\n", + "\n", + "df_individual = pd.concat(dfs_ind, ignore_index=True)\n", + "df_individual = df_individual.dropna(\n", + " subset=['num_output_tokens', 'num_concurrent_requests', 'client_ttft_s']\n", + ")\n", + "df_individual['num_output_tokens'] = df_individual['num_output_tokens'].astype(int)\n", + "df_individual['num_concurrent_requests'] = df_individual['num_concurrent_requests'].astype(int)\n", + "df_individual['out_tokens_label'] = df_individual['num_output_tokens'].astype(str) + ' output tokens'\n", + "df_individual['concurrency_str'] = df_individual['num_concurrent_requests'].astype(str)\n", + "df_individual['is_error'] = df_individual['error_code'].notna()\n", + "df_individual['run_type'] = pd.Categorical(\n", + " df_individual['run_type'],\n", + " categories=['Single Run (1x)', 'Multi Run (10x)'],\n", + " ordered=True\n", + ")\n", + "\n", + "print(f'Loaded {len(df_individual):,} individual request records')\n", + "df_individual.groupby(['label', 'run_type', 'is_error'])[['client_ttft_s']].count().rename(\n", + " columns={'client_ttft_s': 'count'}\n", + ")" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "id": "cell-box-client-ttft", "metadata": {}, - "outputs": [], - "source": "df_box = df_individual[~df_individual['is_error']].copy()\n\nfig = px.box(\n df_box,\n x='concurrency_str',\n y='client_ttft_s',\n color='label',\n facet_col='in_tokens_label',\n color_discrete_map=COLOR_MAP,\n category_orders={\n 'concurrency_str': CONC_STR,\n 'in_tokens_label': INPUT_LABELS,\n },\n points='outliers',\n labels={\n 'concurrency_str': 'Concurrency',\n 'client_ttft_s': 'Client TTFT (s)',\n 'label': 'Model',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'Client TTFT Distribution per Concurrency — faceted by Input Tokens
'\n 'Encrypted shows higher median AND wider spread due to variable TEE network latency. '\n 'Spread grows with both input size and concurrency.'\n ),\n)\nfig.update_layout(height=450, width=1200, template='plotly_white')\nfig.show()" + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "alignmentgroup": "True", + "boxpoints": "outliers", + "hovertemplate": "Model=Encrypted
Run Type=Multi Run (10x)
Output Tokens=100 output tokens
Concurrency=%{x}
Client TTFT (s)=%{y}", + "legendgroup": "Encrypted", + "marker": { + "color": "#EF553B" + }, + "name": "Encrypted", + "notched": false, + "offsetgroup": "Encrypted", + "orientation": "v", + "showlegend": true, + "type": "box", + "x": [ + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "5", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + "16", + 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+ "dtype": "f8" + }, + "y0": " ", + "yaxis": "y" + }, + { + "alignmentgroup": "True", + "boxpoints": "outliers", + "hovertemplate": "Model=Non-Encrypted
Run Type=Multi Run (10x)
Output Tokens=100 output tokens
Concurrency=%{x}
Client TTFT (s)=%{y}", + "legendgroup": "Non-Encrypted", + "marker": { + "color": "#636EFA" + }, + "name": "Non-Encrypted", + "notched": false, + "offsetgroup": "Non-Encrypted", + "orientation": "v", + "showlegend": true, + "type": "box", + "x": [ + "3", + "3", + "3", + "3", + "3", + "3", + "3", + "3", + "3", + "3", + "3", + "3", + "3", + "3", + "3", + "3", + "3", + "3", + "3", + "3", + "3", + "3", + "3", + "3", + "3", + "3", + "3", + "3", + "3", + "3", + "1", + "1", + "1", + "1", + "1", + "1", + "1", + "1", + "1", + "1", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", + "32", 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Encrypted shows higher median AND wider spread due to variable TEE network latency. High concurrency increases spread (TEE queue wait time variance)." + }, + "width": 1100, + "xaxis": { + "anchor": "y", + "categoryarray": [ + "1", + "3", + "5", + "8", + "16", + "32" + ], + "categoryorder": "array", + "domain": [ + 0, + 0.98 + ], + "title": { + "text": "Concurrency" + } + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0, + 0.98 + ], + "matches": "x", + "showticklabels": false + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 0.485 + ], + "title": { + "text": "Client TTFT (s)" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0.515, + 1 + ], + "matches": "y", + "title": { + "text": "Client TTFT (s)" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_box = df_individual[~df_individual['is_error']].copy()\n", + "\n", + "fig = px.box(\n", + " df_box,\n", + " x='concurrency_str',\n", + " y='client_ttft_s',\n", + " color='label',\n", + " facet_col='out_tokens_label',\n", + " facet_row='run_type',\n", + " color_discrete_map=COLOR_MAP,\n", + " category_orders={\n", + " 'concurrency_str': CONC_STR,\n", + " 'run_type': ['Single Run (1x)', 'Multi Run (10x)'],\n", + " },\n", + " points='outliers',\n", + " labels={\n", + " 'concurrency_str': 'Concurrency',\n", + " 'client_ttft_s': 'Client TTFT (s)',\n", + " 'label': 'Model',\n", + " 'out_tokens_label':'Output Tokens',\n", + " 'run_type': 'Run Type',\n", + " },\n", + " title=(\n", + " 'Client TTFT Distribution per Concurrency
'\n", + " 'Encrypted shows higher median AND wider spread due to variable TEE network latency. '\n", + " 'High concurrency increases spread (TEE queue wait time variance).'\n", + " ),\n", + ")\n", + "fig.update_layout(height=700, width=1100, template='plotly_white')\n", + "fig.show()" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "id": "cell-box-server-ttft", "metadata": {}, - "outputs": [], - "source": "fig = px.box(\n df_box,\n x='concurrency_str',\n y='server_ttft_s',\n color='label',\n facet_col='in_tokens_label',\n color_discrete_map=COLOR_MAP,\n category_orders={\n 'concurrency_str': CONC_STR,\n 'in_tokens_label': INPUT_LABELS,\n },\n points='outliers',\n labels={\n 'concurrency_str': 'Concurrency',\n 'server_ttft_s': 'Server TTFT (s)',\n 'label': 'Model',\n 'in_tokens_label': 'Input Tokens',\n },\n title=(\n 'Server TTFT Distribution per Concurrency — faceted by Input Tokens
'\n 'Server-side distribution. At medium concurrency, encrypted may show LOWER server TTFT '\n 'because TEE de-batching reduces the effective batch size seen by the server.'\n ),\n)\nfig.update_layout(height=450, width=1200, template='plotly_white')\nfig.show()" + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "alignmentgroup": "True", + "boxpoints": "outliers", + "hovertemplate": "Model=Encrypted
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Output Tokens=100 output tokens
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Run Type=Multi Run (10x)
Output Tokens=100 output tokens
Concurrency=%{x}
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'\n", + " 'Server-side distribution. At medium concurrencies, encrypted may show LOWER server TTFT '\n", + " 'because TEE de-batching reduces effective batch size seen by the server.'\n", + " ),\n", + ")\n", + "fig.update_layout(height=700, width=1100, template='plotly_white')\n", + "fig.show()" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "id": "cell-scatter-tee", "metadata": {}, - "outputs": [], - "source": "# Per-request: TEE network latency vs server TTFT (encrypted only), one column per Input Tokens\ndf_enc_ind = df_individual[\n (df_individual['label'] == 'Encrypted') &\n (~df_individual['is_error']) &\n (df_individual['server_network_latency_ms'].notna())\n].copy()\n\nfig = px.scatter(\n df_enc_ind,\n x='server_ttft_s',\n y='server_network_latency_ms',\n color='concurrency_str',\n facet_col='in_tokens_label',\n opacity=0.55,\n category_orders={\n 'concurrency_str': CONC_STR,\n 'in_tokens_label': INPUT_LABELS,\n },\n labels={\n 'server_ttft_s': 'Server TTFT (s)',\n 'server_network_latency_ms': 'TEE Network Latency (ms)',\n 'in_tokens_label': 'Input Tokens',\n 'concurrency_str': 'Concurrency',\n },\n title=(\n 'Per-Request: TEE Network Latency vs Server TTFT — Encrypted (faceted by Input Tokens)
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Shows how network latency varies independently from server TTFT. A flat horizontal spread would indicate network latency is independent of server load." + }, + "width": 1400, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 0.15 + ], + "title": { + "text": "Server TTFT (s)" + } + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0.16999999999999998, + 0.31999999999999995 + ], + "matches": "x", + "title": { + "text": "Server TTFT (s)" + } + }, + "xaxis3": { + "anchor": "y3", + "domain": [ + 0.33999999999999997, + 0.49 + ], + "matches": "x", + "title": { + "text": "Server TTFT (s)" + } + }, + "xaxis4": { + "anchor": "y4", + "domain": [ + 0.51, + 0.66 + ], + "matches": "x", + "title": { + "text": "Server TTFT (s)" + } + }, + "xaxis5": { + "anchor": "y5", + "domain": [ + 0.6799999999999999, + 0.83 + ], + "matches": "x", + "title": { + "text": "Server TTFT (s)" + } + }, + "xaxis6": { + "anchor": "y6", + "domain": [ + 0.85, + 1 + ], + "matches": "x", + "title": { + "text": "Server TTFT (s)" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "TEE Network Latency (ms)" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis3": { + "anchor": "x3", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis4": { + "anchor": "x4", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis5": { + "anchor": "x5", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis6": { + "anchor": "x6", + "domain": [ + 0, + 1 + ], + "matches": "y", + "showticklabels": false + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Per-request: TEE network latency vs server TTFT (encrypted multi run only)\n", + "df_enc_ind = df_individual[\n", + " (df_individual['label'] == 'Encrypted') &\n", + " (df_individual['run_type'] == 'Multi Run (10x)') &\n", + " (~df_individual['is_error']) &\n", + " (df_individual['server_network_latency_ms'].notna())\n", + "].copy()\n", + "\n", + "fig = px.scatter(\n", + " df_enc_ind,\n", + " x='server_ttft_s',\n", + " y='server_network_latency_ms',\n", + " color='out_tokens_label',\n", + " facet_col='concurrency_str',\n", + " opacity=0.55,\n", + " category_orders={'concurrency_str': CONC_STR},\n", + " labels={\n", + " 'server_ttft_s': 'Server TTFT (s)',\n", + " 'server_network_latency_ms': 'TEE Network Latency (ms)',\n", + " 'out_tokens_label': 'Output Tokens',\n", + " 'concurrency_str': 'Concurrency',\n", + " },\n", + " title=(\n", + " 'Per-Request: TEE Network Latency vs Server TTFT — Encrypted Multi Run
'\n", + " 'Shows how network latency varies independently from server TTFT. '\n", + " 'A flat horizontal spread would indicate network latency is independent of server load.'\n", + " ),\n", + ")\n", + "fig.update_layout(height=420, width=1400, template='plotly_white')\n", + "fig.show()" + ] }, { "cell_type": "markdown", @@ -237,11 +26304,87 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "id": "cell-summary-table", "metadata": {}, - "outputs": [], - "source": "DIMS = ['num_input_tokens', 'num_output_tokens', 'num_concurrent_requests']\n\nALL_METRICS = [\n ('server_ttft_s_p50', 'Server TTFT p50 (s)'),\n ('server_output_token_per_s_p50', 'Server Tok/s p50'),\n ('server_end_to_end_latency_s_p50', 'Server E2E p50 (s)'),\n ('client_ttft_s_p50', 'Client TTFT p50 (s)'),\n ('client_end_to_end_latency_s_p50', 'Client E2E p50 (s)'),\n ('client_output_token_per_s_p50', 'Client Tok/s p50'),\n ('client_total_output_throughput', 'Total Throughput (tok/s)'),\n ('num_completed_requests_per_min', 'Req / min'),\n ('error_rate_pct', 'Error Rate (%)'),\n]\n\ndf_enc_s = df_all[df_all['label'] == 'Encrypted'].set_index(DIMS)\ndf_plain_s = df_all[df_all['label'] == 'Non-Encrypted'].set_index(DIMS)\n\nrows = []\nfor col, label in ALL_METRICS:\n if col not in df_enc_s.columns:\n continue\n for idx in df_enc_s.index:\n if idx not in df_plain_s.index:\n continue\n enc_v = df_enc_s.loc[idx, col]\n plain_v = df_plain_s.loc[idx, col]\n if pd.isna(enc_v) and pd.isna(plain_v):\n continue\n delta = enc_v - plain_v if pd.notna(enc_v) and pd.notna(plain_v) else np.nan\n pct_chg = delta / plain_v * 100 if pd.notna(delta) and plain_v != 0 else np.nan\n rows.append({\n 'Metric': label,\n 'Input Tokens': idx[0],\n 'Output Tokens': idx[1],\n 'Concurrency': idx[2],\n 'Non-Encrypted': plain_v,\n 'Encrypted': enc_v,\n 'Delta': delta,\n '% Change': pct_chg,\n })\n\ndf_summary = pd.DataFrame(rows).sort_values(\n ['Metric', 'Input Tokens', 'Concurrency']\n)\n\ndef _pct_color(val):\n if pd.isna(val): return ''\n if val > 100: return 'background-color: #ff6666'\n if val > 50: return 'background-color: #ffaaaa'\n if val > 20: return 'background-color: #ffd8aa'\n if val > 0: return 'background-color: #fffacc'\n if val < -20: return 'background-color: #aaffaa'\n return ''\n\ndf_summary.style \\\n .applymap(_pct_color, subset=['% Change']) \\\n .format({\n 'Non-Encrypted': '{:.3f}',\n 'Encrypted': '{:.3f}',\n 'Delta': '{:+.3f}',\n '% Change': '{:+.1f}%',\n }, na_rep='—')" + "outputs": [ + { + "ename": "ValueError", + "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", + "\u001b[32m/var/folders/g3/mcw1yd_565l486ztq80gf9kw0000gp/T/ipykernel_95595/3116414831.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 23\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m idx \u001b[38;5;28;01mnot\u001b[39;00m \u001b[38;5;28;01min\u001b[39;00m df_plain_s.index:\n\u001b[32m 24\u001b[39m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[32m 25\u001b[39m enc_v = df_enc_s.loc[idx, col]\n\u001b[32m 26\u001b[39m plain_v = df_plain_s.loc[idx, col]\n\u001b[32m---> \u001b[39m\u001b[32m27\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m pd.isna(enc_v) \u001b[38;5;28;01mand\u001b[39;00m pd.isna(plain_v):\n\u001b[32m 28\u001b[39m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[32m 29\u001b[39m delta = enc_v - plain_v \u001b[38;5;28;01mif\u001b[39;00m pd.notna(enc_v) \u001b[38;5;28;01mand\u001b[39;00m pd.notna(plain_v) \u001b[38;5;28;01melse\u001b[39;00m np.nan\n\u001b[32m 30\u001b[39m pct_chg = delta / plain_v * \u001b[32m100\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m pd.notna(delta) \u001b[38;5;28;01mand\u001b[39;00m plain_v != \u001b[32m0\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m np.nan\n", + "\u001b[32m~/github_repos/ai-starter-kit_copy/benchmarking/.venv/lib/python3.11/site-packages/pandas/core/generic.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 1525\u001b[39m @final\n\u001b[32m 1526\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m __nonzero__(self) -> NoReturn:\n\u001b[32m-> \u001b[39m\u001b[32m1527\u001b[39m raise ValueError(\n\u001b[32m 1528\u001b[39m \u001b[33mf\"The truth value of a {type(self).__name__} is ambiguous. \"\u001b[39m\n\u001b[32m 1529\u001b[39m \u001b[33m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[39m\n\u001b[32m 1530\u001b[39m )\n", + "\u001b[31mValueError\u001b[39m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()." + ] + } + ], + "source": [ + "DIMS = ['run_type', 'num_output_tokens', 'num_concurrent_requests']\n", + "\n", + "ALL_METRICS = [\n", + " ('server_ttft_s_p50', 'Server TTFT p50 (s)'),\n", + " ('server_output_token_per_s_p50', 'Server Tok/s p50'),\n", + " ('server_end_to_end_latency_s_p50', 'Server E2E p50 (s)'),\n", + " ('client_ttft_s_p50', 'Client TTFT p50 (s)'),\n", + " ('client_end_to_end_latency_s_p50', 'Client E2E p50 (s)'),\n", + " ('client_output_token_per_s_p50', 'Client Tok/s p50'),\n", + " ('client_total_output_throughput', 'Total Throughput (tok/s)'),\n", + " ('num_completed_requests_per_min', 'Req / min'),\n", + " ('error_rate_pct', 'Error Rate (%)'),\n", + "]\n", + "\n", + "df_enc_s = df_all[df_all['label'] == 'Encrypted'].set_index(DIMS)\n", + "df_plain_s = df_all[df_all['label'] == 'Non-Encrypted'].set_index(DIMS)\n", + "\n", + "rows = []\n", + "for col, label in ALL_METRICS:\n", + " if col not in df_enc_s.columns:\n", + " continue\n", + " for idx in df_enc_s.index:\n", + " if idx not in df_plain_s.index:\n", + " continue\n", + " enc_v = df_enc_s.loc[idx, col]\n", + " plain_v = df_plain_s.loc[idx, col]\n", + " if pd.isna(enc_v) and pd.isna(plain_v):\n", + " continue\n", + " delta = enc_v - plain_v if pd.notna(enc_v) and pd.notna(plain_v) else np.nan\n", + " pct_chg = delta / plain_v * 100 if pd.notna(delta) and plain_v != 0 else np.nan\n", + " rows.append({\n", + " 'Metric': label,\n", + " 'Run Type': idx[0],\n", + " 'Output Tokens': idx[1],\n", + " 'Concurrency': idx[2],\n", + " 'Non-Encrypted': plain_v,\n", + " 'Encrypted': enc_v,\n", + " 'Delta': delta,\n", + " '% Change': pct_chg,\n", + " })\n", + "\n", + "df_summary = pd.DataFrame(rows).sort_values(\n", + " ['Metric', 'Run Type', 'Output Tokens', 'Concurrency']\n", + ")\n", + "\n", + "def _pct_color(val):\n", + " if pd.isna(val): return ''\n", + " if val > 100: return 'background-color: #ff6666'\n", + " if val > 50: return 'background-color: #ffaaaa'\n", + " if val > 20: return 'background-color: #ffd8aa'\n", + " if val > 0: return 'background-color: #fffacc'\n", + " if val < -20: return 'background-color: #aaffaa'\n", + " return ''\n", + "\n", + "df_summary.style \\\n", + " .applymap(_pct_color, subset=['% Change']) \\\n", + " .format({\n", + " 'Non-Encrypted': '{:.3f}',\n", + " 'Encrypted': '{:.3f}',\n", + " 'Delta': '{:+.3f}',\n", + " '% Change': '{:+.1f}%',\n", + " }, na_rep='—')" + ] }, { "cell_type": "markdown", @@ -253,11 +26396,912 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "id": "cell-tee-overhead-table", "metadata": {}, - "outputs": [], - "source": "DIMS = ['num_input_tokens', 'num_output_tokens', 'num_concurrent_requests']\n\ndf_tee_oh = df_all[df_all['label'] == 'Encrypted'].copy()\ndf_tee_oh = df_tee_oh.merge(\n df_all[df_all['label'] == 'Non-Encrypted'][DIMS + ['client_ttft_s_p50']].rename(\n columns={'client_ttft_s_p50': 'client_ttft_plain'}\n ),\n on=DIMS,\n)\n\ndf_tee_oh['enc_time_s'] = df_tee_oh['total_encryption_time_ms_mean'] / 1000\ndf_tee_oh['dec_time_s'] = df_tee_oh['total_decryption_time_ms_mean'] / 1000\ndf_tee_oh['network_s'] = df_tee_oh['server_network_latency_ms_mean'] / 1000\ndf_tee_oh['tee_total_s'] = df_tee_oh['enc_time_s'] + df_tee_oh['dec_time_s'] + df_tee_oh['network_s']\ndf_tee_oh['ttft_delta_s'] = df_tee_oh['client_ttft_s_p50'] - df_tee_oh['client_ttft_plain']\ndf_tee_oh['network_pct'] = df_tee_oh['network_s'] / df_tee_oh['client_ttft_s_p50'] * 100\ndf_tee_oh['enc_pct'] = df_tee_oh['enc_time_s'] / df_tee_oh['client_ttft_s_p50'] * 100\ndf_tee_oh['dec_pct'] = df_tee_oh['dec_time_s'] / df_tee_oh['client_ttft_s_p50'] * 100\ndf_tee_oh['tee_total_pct'] = df_tee_oh['tee_total_s'] / df_tee_oh['client_ttft_s_p50'] * 100\n\nDISPLAY = {\n 'num_input_tokens': 'Input Tokens',\n 'num_output_tokens': 'Output Tokens',\n 'num_concurrent_requests': 'Concurrency',\n 'client_ttft_plain': 'Plain TTFT (s)',\n 'client_ttft_s_p50': 'Enc TTFT (s)',\n 'ttft_delta_s': 'Delta (s)',\n 'enc_time_s': 'Encryption (s)',\n 'dec_time_s': 'Decryption (s)',\n 'network_s': 'Network Hop (s)',\n 'tee_total_s': 'TEE Total (s)',\n 'tee_total_pct': 'TEE % of Enc TTFT',\n 'network_pct': 'Network %',\n 'enc_pct': 'Encrypt %',\n 'dec_pct': 'Decrypt %',\n}\n\ndf_tee_display = (\n df_tee_oh[list(DISPLAY.keys())]\n .rename(columns=DISPLAY)\n .sort_values(['Input Tokens', 'Concurrency'])\n)\n\npct_cols = [c for c in df_tee_display.columns if '%' in c]\ns_cols = [c for c in df_tee_display.columns if '(s)' in c]\n\ndf_tee_display.style \\\n .background_gradient(subset=['TEE % of Enc TTFT'], cmap='YlOrRd') \\\n .format({c: '{:.1f}%' for c in pct_cols}) \\\n .format({c: '{:.4f}' for c in s_cols}, na_rep='—')" + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + 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 Run TypeOutput TokensConcurrencyPlain TTFT (s)Enc TTFT (s)Delta (s)Encryption (s)Decryption (s)Network Hop (s)TEE Total (s)TEE % of Enc TTFTNetwork %Encrypt %Decrypt %
0Multi Run (10x)10010.25430.37950.12520.01220.00180.33080.344990.87602187.1711203.2208170.484084
1Multi Run (10x)10010.35310.37950.02640.01220.00180.33080.344990.87602187.1711203.2208170.484084
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\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DIMS = ['run_type', 'num_output_tokens', 'num_concurrent_requests']\n", + "\n", + "df_tee_oh = df_all[df_all['label'] == 'Encrypted'].copy()\n", + "df_tee_oh = df_tee_oh.merge(\n", + " df_all[df_all['label'] == 'Non-Encrypted'][DIMS + ['client_ttft_s_p50']].rename(\n", + " columns={'client_ttft_s_p50': 'client_ttft_plain'}\n", + " ),\n", + " on=DIMS,\n", + ")\n", + "\n", + "df_tee_oh['enc_time_s'] = df_tee_oh['total_encryption_time_ms_mean'] / 1000\n", + "df_tee_oh['dec_time_s'] = df_tee_oh['total_decryption_time_ms_mean'] / 1000\n", + "df_tee_oh['network_s'] = df_tee_oh['server_network_latency_ms_mean'] / 1000\n", + "df_tee_oh['tee_total_s'] = df_tee_oh['enc_time_s'] + df_tee_oh['dec_time_s'] + df_tee_oh['network_s']\n", + "df_tee_oh['ttft_delta_s'] = df_tee_oh['client_ttft_s_p50'] - df_tee_oh['client_ttft_plain']\n", + "df_tee_oh['network_pct'] = df_tee_oh['network_s'] / df_tee_oh['client_ttft_s_p50'] * 100\n", + "df_tee_oh['enc_pct'] = df_tee_oh['enc_time_s'] / df_tee_oh['client_ttft_s_p50'] * 100\n", + "df_tee_oh['dec_pct'] = df_tee_oh['dec_time_s'] / df_tee_oh['client_ttft_s_p50'] * 100\n", + "df_tee_oh['tee_total_pct'] = df_tee_oh['tee_total_s'] / df_tee_oh['client_ttft_s_p50'] * 100\n", + "\n", + "DISPLAY = {\n", + " 'run_type': 'Run Type',\n", + " 'num_output_tokens': 'Output Tokens',\n", + " 'num_concurrent_requests': 'Concurrency',\n", + " 'client_ttft_plain': 'Plain TTFT (s)',\n", + " 'client_ttft_s_p50': 'Enc TTFT (s)',\n", + " 'ttft_delta_s': 'Delta (s)',\n", + " 'enc_time_s': 'Encryption (s)',\n", + " 'dec_time_s': 'Decryption (s)',\n", + " 'network_s': 'Network Hop (s)',\n", + " 'tee_total_s': 'TEE Total (s)',\n", + " 'tee_total_pct': 'TEE % of Enc TTFT',\n", + " 'network_pct': 'Network %',\n", + " 'enc_pct': 'Encrypt %',\n", + " 'dec_pct': 'Decrypt %',\n", + "}\n", + "\n", + "df_tee_display = (\n", + " df_tee_oh[list(DISPLAY.keys())]\n", + " .rename(columns=DISPLAY)\n", + " .sort_values(['Run Type', 'Output Tokens', 'Concurrency'])\n", + ")\n", + "\n", + "pct_cols = [c for c in df_tee_display.columns if '%' in c]\n", + "s_cols = [c for c in df_tee_display.columns if '(s)' in c]\n", + "\n", + "df_tee_display.style \\\n", + " .background_gradient(subset=['TEE % of Enc TTFT'], cmap='YlOrRd') \\\n", + " .format({c: '{:.1f}%' for c in pct_cols}) \\\n", + " .format({c: '{:.4f}' for c in s_cols}, na_rep='—')" + ] }, { "cell_type": "code", @@ -282,6 +27326,14 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c4e3c11", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -305,4 +27357,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +}