IMAGE_ANALYSIS_OPTIMIZATION

Image Analysis Plugin Optimization Guide

Date: December 2025
Version: 1.0.0
Category: Performance / Optimization
Audience: Plugin Developers

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Overview

This guide provides comprehensive optimization strategies for image analysis plugins in ThemisDB, covering memory management, compute efficiency, and architectural best practices.

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Memory Optimization

1. Zero-Copy Data Transfer

Problem: Copying image data between CPU and GPU is expensive.

Solution: Use pinned memory and unified memory.

class OptimizedImageBackend : public IImageAnalysisBackend {
private:
    // Pinned memory for zero-copy transfer
    uint8_t* pinned_buffer_;
    size_t buffer_size_;
    
public:
    bool initialize(const PluginConfig& config, BackendType backend) override {
        buffer_size_ = 4 * 1024 * 1024;  // 4MB buffer
        
        #ifdef USE_CUDA
        // Allocate pinned memory for faster CPU↔GPU transfer
        cudaHostAlloc(&pinned_buffer_, buffer_size_, cudaHostAllocMapped);
        #else
        pinned_buffer_ = new uint8_t[buffer_size_];
        #endif
        
        return true;
    }
    
    EmbeddingResult generateEmbedding(
        const std::vector<uint8_t>& image_data,
        const ImageMetadata* metadata
    ) override {
        // Copy to pinned memory once
        if (image_data.size() <= buffer_size_) {
            std::memcpy(pinned_buffer_, image_data.data(), image_data.size());
            
            // GPU can now access pinned_buffer_ directly (zero-copy)
            return processImage(pinned_buffer_, image_data.size());
        }
        
        // Fallback for large images
        return processImageDirect(image_data);
    }
    
    ~OptimizedImageBackend() {
        #ifdef USE_CUDA
        if (pinned_buffer_) {
            cudaFreeHost(pinned_buffer_);
        }
        #else
        delete[] pinned_buffer_;
        #endif
    }
};

Impact: 2-5x faster data transfer, especially for batch processing.

---

2. Memory Pool Allocation

Problem: Frequent allocation/deallocation causes fragmentation.

Solution: Pre-allocate memory pools.

class MemoryPoolBackend : public IImageAnalysisBackend {
private:
    // Memory pool for embeddings
    std::vector<std::vector<float>> embedding_pool_;
    std::atomic<size_t> pool_index_{0};
    static constexpr size_t POOL_SIZE = 16;
    static constexpr size_t EMBEDDING_DIM = 512;
    
public:
    bool initialize(const PluginConfig& config, BackendType backend) override {
        // Pre-allocate embedding buffers
        embedding_pool_.resize(POOL_SIZE);
        for (auto& emb : embedding_pool_) {
            emb.resize(EMBEDDING_DIM);
        }
        return true;
    }
    
    EmbeddingResult generateEmbedding(
        const std::vector<uint8_t>& image_data,
        const ImageMetadata* metadata
    ) override {
        // Get buffer from pool (round-robin)
        size_t idx = pool_index_.fetch_add(1) % POOL_SIZE;
        auto& buffer = embedding_pool_[idx];
        
        // Reuse existing buffer (no allocation!)
        computeEmbedding(image_data, buffer);
        
        EmbeddingResult result;
        result.success = true;
        result.dimension = EMBEDDING_DIM;
        result.embedding = buffer;  // Copy-on-write semantics
        
        return result;
    }
};

Impact: Reduces allocation overhead by 80-90%, smoother latency.

---

3. Model Weight Sharing

Problem: Multiple plugin instances load same model.

Solution: Share model weights across instances.

class SharedWeightsBackend : public IImageAnalysisBackend {
private:
    // Shared model weights (static)
    static std::shared_ptr<ModelWeights> shared_weights_;
    static std::mutex weights_mutex_;
    
    // Instance-specific state
    std::unique_ptr<InferenceContext> context_;
    
public:
    bool initialize(const PluginConfig& config, BackendType backend) override {
        std::lock_guard<std::mutex> lock(weights_mutex_);
        
        // Load weights only once
        if (!shared_weights_) {
            std::string model_path = config.get<std::string>("model_path", "");
            shared_weights_ = std::make_shared<ModelWeights>(model_path);
        }
        
        // Create instance-specific inference context
        context_ = std::make_unique<InferenceContext>(shared_weights_, backend);
        
        return true;
    }
};

// Initialize static members
std::shared_ptr<ModelWeights> SharedWeightsBackend::shared_weights_;
std::mutex SharedWeightsBackend::weights_mutex_;

Impact: Reduces memory by 70-80% for multiple plugin instances.

---

Compute Optimization

1. Batch Processing Optimization

Problem: Single-image inference underutilizes GPU.

Solution: Implement efficient batching.

class BatchOptimizedBackend : public IImageAnalysisBackend {
private:
    // Batch queue
    struct BatchItem {
        std::vector<uint8_t> image_data;
        std::promise<EmbeddingResult> promise;
    };
    
    std::queue<BatchItem> batch_queue_;
    std::mutex queue_mutex_;
    std::condition_variable queue_cv_;
    std::thread batch_thread_;
    std::atomic<bool> running_{false};
    
    static constexpr size_t OPTIMAL_BATCH_SIZE = 16;
    static constexpr int BATCH_TIMEOUT_MS = 10;
    
    void processBatchLoop() {
        while (running_) {
            std::vector<BatchItem> batch;
            
            {
                std::unique_lock<std::mutex> lock(queue_mutex_);
                
                // Wait for batch or timeout
                queue_cv_.wait_for(lock, 
                    std::chrono::milliseconds(BATCH_TIMEOUT_MS),
                    [this]() { 
                        return batch_queue_.size() >= OPTIMAL_BATCH_SIZE || !running_;
                    });
                
                // Collect batch
                while (!batch_queue_.empty() && batch.size() < OPTIMAL_BATCH_SIZE) {
                    batch.push_back(std::move(batch_queue_.front()));
                    batch_queue_.pop();
                }
            }
            
            if (batch.empty()) continue;
            
            // Process batch (GPU-optimized)
            auto results = processBatch(batch);
            
            // Set promises
            for (size_t i = 0; i < batch.size(); ++i) {
                batch[i].promise.set_value(std::move(results[i]));
            }
        }
    }
    
public:
    bool initialize(const PluginConfig& config, BackendType backend) override {
        running_ = true;
        batch_thread_ = std::thread(&BatchOptimizedBackend::processBatchLoop, this);
        return true;
    }
    
    EmbeddingResult generateEmbedding(
        const std::vector<uint8_t>& image_data,
        const ImageMetadata* metadata
    ) override {
        BatchItem item;
        item.image_data = image_data;
        auto future = item.promise.get_future();
        
        {
            std::lock_guard<std::mutex> lock(queue_mutex_);
            batch_queue_.push(std::move(item));
        }
        queue_cv_.notify_one();
        
        return future.get();
    }
    
    void shutdown() override {
        running_ = false;
        queue_cv_.notify_all();
        if (batch_thread_.joinable()) {
            batch_thread_.join();
        }
    }
};

Impact: 2-4x throughput improvement for concurrent requests.

---

2. Model Quantization

Problem: FP32 models are slow and memory-intensive.

Solution: Use INT8 or FP16 quantization.

class QuantizedBackend : public IImageAnalysisBackend {
private:
    enum class Precision {
        FP32,  // Full precision
        FP16,  // Half precision (2x faster, 2x less memory)
        INT8   // 8-bit (4x faster, 4x less memory)
    };
    
    Precision precision_;
    
public:
    bool initialize(const PluginConfig& config, BackendType backend) override {
        std::string prec_str = config.get<std::string>("precision", "FP16");
        
        if (prec_str == "FP32") {
            precision_ = Precision::FP32;
        } else if (prec_str == "FP16") {
            precision_ = Precision::FP16;
            // Enable FP16 mode
            #ifdef USE_CUDA
            cudaSetDeviceFlags(cudaDeviceScheduleBlockingSync);
            #endif
        } else if (prec_str == "INT8") {
            precision_ = Precision::INT8;
            // Load calibration data for INT8
            loadCalibrationData(config.get<std::string>("calibration_file", ""));
        }
        
        return loadModel(config, precision_);
    }
};

Configuration:

plugins:
  - name: optimized_clip
    config:
      precision: "FP16"  # or "INT8"
      calibration_file: "./calibration/clip_int8.cache"

Impact:

• FP16: 2x faster, 2x less memory, <1% quality loss
• INT8: 4x faster, 4x less memory, 2-5% quality loss

---

3. Kernel Fusion

Problem: Multiple GPU kernel launches have overhead.

Solution: Fuse operations into single kernels.

// Before: Separate operations
__global__ void preprocessKernel(uint8_t* input, float* output, int size);
__global__ void normalizeKernel(float* data, int size);
__global__ void resizeKernel(float* input, float* output, int w, int h);

// After: Fused kernel
__global__ void fusedPreprocessKernel(
    uint8_t* input, 
    float* output, 
    int in_w, int in_h,
    int out_w, int out_h,
    const float* mean,
    const float* std
) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    
    if (idx < out_w * out_h) {
        // Bilinear resize + normalize in one pass
        int out_y = idx / out_w;
        int out_x = idx % out_w;
        
        float in_x = out_x * float(in_w) / out_w;
        float in_y = out_y * float(in_h) / out_h;
        
        // Bilinear interpolation
        float val = bilinearSample(input, in_x, in_y, in_w, in_h);
        
        // Normalize
        int channel = idx / (out_w * out_h);
        output[idx] = (val / 255.0f - mean[channel]) / std[channel];
    }
}

Impact: 20-30% faster preprocessing, reduced memory bandwidth.

---

Architecture Optimization

1. Asynchronous Execution

Problem: Synchronous calls block caller thread.

Solution: Implement async API with futures.

class AsyncBackend : public IImageAnalysisBackend {
private:
    // Thread pool for async execution
    ThreadPool thread_pool_;
    
public:
    bool initialize(const PluginConfig& config, BackendType backend) override {
        int num_threads = config.get<int>("async_threads", 4);
        thread_pool_.start(num_threads);
        return true;
    }
    
    // Synchronous API (blocks)
    EmbeddingResult generateEmbedding(
        const std::vector<uint8_t>& image_data,
        const ImageMetadata* metadata
    ) override {
        return generateEmbeddingAsync(image_data, metadata).get();
    }
    
    // Asynchronous API (non-blocking)
    std::future<EmbeddingResult> generateEmbeddingAsync(
        const std::vector<uint8_t>& image_data,
        const ImageMetadata* metadata = nullptr
    ) {
        return thread_pool_.enqueue([this, image_data, metadata]() {
            return this->processImageInternal(image_data, metadata);
        });
    }
};

Usage:

// Fire and forget multiple requests
std::vector<std::future<EmbeddingResult>> futures;
for (const auto& img : images) {
    futures.push_back(backend->generateEmbeddingAsync(img));
}

// Collect results
for (auto& future : futures) {
    auto result = future.get();
    process(result);
}

Impact: Better CPU utilization, higher throughput.

---

2. Caching Strategy

Problem: Repeated inference on same images wastes compute.

Solution: Implement LRU cache.

class CachedBackend : public IImageAnalysisBackend {
private:
    struct CacheEntry {
        std::vector<float> embedding;
        std::chrono::steady_clock::time_point timestamp;
    };
    
    // LRU cache
    std::unordered_map<std::string, CacheEntry> cache_;
    std::list<std::string> lru_list_;
    std::mutex cache_mutex_;
    
    size_t max_cache_size_;
    std::chrono::seconds cache_ttl_;
    
    std::string computeHash(const std::vector<uint8_t>& data) {
        // Fast hash (xxHash or similar)
        return xxh64(data.data(), data.size());
    }
    
public:
    bool initialize(const PluginConfig& config, BackendType backend) override {
        max_cache_size_ = config.get<size_t>("cache_size", 1000);
        cache_ttl_ = std::chrono::seconds(config.get<int>("cache_ttl", 3600));
        return true;
    }
    
    EmbeddingResult generateEmbedding(
        const std::vector<uint8_t>& image_data,
        const ImageMetadata* metadata
    ) override {
        std::string hash = computeHash(image_data);
        
        // Check cache
        {
            std::lock_guard<std::mutex> lock(cache_mutex_);
            auto it = cache_.find(hash);
            if (it != cache_.end()) {
                auto age = std::chrono::steady_clock::now() - it->second.timestamp;
                if (age < cache_ttl_) {
                    // Cache hit!
                    EmbeddingResult result;
                    result.success = true;
                    result.embedding = it->second.embedding;
                    result.dimension = it->second.embedding.size();
                    result.inference_time_ms = 0;  // Cached
                    return result;
                }
            }
        }
        
        // Cache miss - compute
        auto result = computeEmbedding(image_data);
        
        // Update cache
        if (result.success) {
            std::lock_guard<std::mutex> lock(cache_mutex_);
            
            // Evict if full
            while (cache_.size() >= max_cache_size_) {
                cache_.erase(lru_list_.back());
                lru_list_.pop_back();
            }
            
            // Add to cache
            CacheEntry entry;
            entry.embedding = result.embedding;
            entry.timestamp = std::chrono::steady_clock::now();
            cache_[hash] = entry;
            lru_list_.push_front(hash);
        }
        
        return result;
    }
};

Impact: Near-zero latency for cached images, reduces GPU load.

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Profiling and Monitoring

1. Built-in Profiling

class ProfiledBackend : public IImageAnalysisBackend {
private:
    struct PerformanceMetrics {
        std::atomic<size_t> total_inferences{0};
        std::atomic<int64_t> total_time_ms{0};
        std::atomic<size_t> cache_hits{0};
        std::atomic<size_t> cache_misses{0};
        
        // Histogram bins (in ms)
        std::array<std::atomic<size_t>, 10> latency_histogram{};
    };
    
    PerformanceMetrics metrics_;
    
    void recordLatency(int64_t latency_ms) {
        metrics_.total_inferences++;
        metrics_.total_time_ms += latency_ms;
        
        // Update histogram
        size_t bin = std::min<size_t>(latency_ms / 10, 9);
        metrics_.latency_histogram[bin]++;
    }
    
public:
    EmbeddingResult generateEmbedding(
        const std::vector<uint8_t>& image_data,
        const ImageMetadata* metadata
    ) override {
        auto start = std::chrono::steady_clock::now();
        
        auto result = computeEmbedding(image_data);
        
        auto end = std::chrono::steady_clock::now();
        auto latency = std::chrono::duration_cast<std::chrono::milliseconds>(
            end - start
        ).count();
        
        recordLatency(latency);
        result.inference_time_ms = latency;
        
        return result;
    }
    
    nlohmann::json getStatistics() const override {
        size_t total = metrics_.total_inferences.load();
        int64_t total_time = metrics_.total_time_ms.load();
        
        nlohmann::json stats;
        stats["total_inferences"] = total;
        stats["average_latency_ms"] = total > 0 ? total_time / total : 0;
        stats["cache_hit_rate"] = total > 0 ? 
            double(metrics_.cache_hits) / total : 0.0;
        
        // Latency histogram
        stats["latency_histogram"] = nlohmann::json::object();
        for (size_t i = 0; i < metrics_.latency_histogram.size(); ++i) {
            std::string bucket = std::to_string(i * 10) + "-" + 
                                std::to_string((i + 1) * 10) + "ms";
            stats["latency_histogram"][bucket] = metrics_.latency_histogram[i].load();
        }
        
        return stats;
    }
};

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Best Practices Checklist

Plugin Development

• Use pinned memory for CPU↔GPU transfers
• Implement memory pooling for frequent allocations
• Share model weights across plugin instances
• Enable FP16/INT8 quantization when possible
• Fuse preprocessing operations into single kernels
• Implement async API with thread pool
• Add LRU cache for repeated inferences
• Include built-in profiling/metrics
• Test with realistic workloads
• Profile with production data

Configuration

• Tune batch size for your hardware (16-32 typical)
• Set appropriate cache size (1000-10000 entries)
• Configure warmup iterations (10-20)
• Set thread pool size (cores - 2)
• Enable appropriate quantization
• Configure memory limits
• Set timeout values
• Enable logging for debugging

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References

• CUDA Best Practices
• ONNX Runtime Performance Tuning
• TensorRT Optimization Guide
• llama.cpp Optimization