performance_gpu_plan

GPU Acceleration Plan - ThemisDB

Stand: 22. Dezember 2025
Version: v1.3.0
Kategorie: ⚡ Performance
Status: Planning Phase
Priorität: P0 (Q2 2026)

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📑 Table of Contents

• Executive Summary
• Backends
• Roadmap

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Executive Summary

ThemisDB plant die Integration von GPU-Beschleunigung für kritische Performance-Bottlenecks:

• Vector Search (CUDA/Faiss GPU) - 10-50x Speedup
• Geo Operations (CUDA Spatial Kernels) - 5-20x Speedup
• DirectX Compute (Windows Fallback) - Native Windows GPU Support

Erwarteter ROI:

• Batch Vector Search: 1,800 → 50,000+ queries/s
• Spatial Queries: 5,000 → 50,000+ ops/s
• Total Cost: $50K-$100K (Hardware + Development)

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1. GPU Vector Search (CUDA/Faiss GPU)

1.1 Hardware Requirements

Minimum:

• GPU: NVIDIA GPU with Compute Capability 7.0+ (Volta: V100, T4)
• VRAM: 8GB
• CUDA: 11.0+
• Driver: 450.80.02+

Recommended:

• GPU: A100 (80GB), RTX 4090 (24GB), or H100
• VRAM: 16GB+
• CUDA: 12.0+
• Multi-GPU: 2-4 GPUs for parallel processing

Performance Expectations:

Hardware	Vectors	Batch Size	Throughput	Latency (p50)
CPU (i7-12700K)	1M	100	1,800 q/s	0.55 ms
T4 (16GB)	1M	1000	25,000 q/s	0.04 ms
A100 (40GB)	10M	5000	100,000 q/s	0.05 ms

1.2 Implementation Timeline

Phase 1: Faiss GPU Integration (4 weeks)

• Add Faiss GPU dependency
• Implement GPUVectorIndex class
• GPU memory management
• Index build on GPU
• Batch query API

Phase 2: CUDA Custom Kernels (2 weeks)

• CUDA kernel for distance computation
• Memory optimization
• Warp-level primitives

Phase 3: Integration & Testing (2 weeks)

• VectorIndexManager integration
• Configuration support
• Benchmark suite
• Error handling

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2. DirectX Compute Shaders (Windows)

2.1 Motivation

• Windows-native GPU acceleration
• Fallback when CUDA not available
• DirectML for ML workloads
• Wider GPU compatibility (AMD, Intel)

2.2 Hardware Requirements

Minimum:

• Windows 10 (1809+) or Windows 11
• DirectX 12 capable GPU
• Driver: WDDM 2.5+

Expected Performance:

• 70-90% of CUDA performance
• Better compatibility with non-NVIDIA GPUs

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3. Geo Operations GPU Acceleration

3.1 Operations to Accelerate

• Distance calculations (haversine, vincenty)
• Point-in-polygon tests
• R-Tree spatial queries
• Geohash encoding/decoding
• KNN spatial search

Expected Speedup: 5-20x for complex spatial queries

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4. Cost Analysis

Hardware Cost (One-time):

• T4 (16GB): ~$2,500
• RTX 4090 (24GB): ~$1,600
• A100 (40GB): ~$10,000

Development Cost:

• Phase 1 (Faiss): 4 weeks × $10K = $40K
• Phase 2 (CUDA): 2 weeks × $10K = $20K
• Phase 3 (Testing): 2 weeks × $10K = $20K
• Total: $80K development + $2.5K-$10K hardware

ROI:

• 10-50x performance improvement
• Reduced infrastructure costs
• Better user experience

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5. Timeline & Milestones

Q2 2026 (April - June)

April 2026:

• Week 1-2: Faiss GPU Integration
• Week 3-4: CUDA Custom Kernels

May 2026:

• Week 1-2: Integration & Testing
• Week 3-4: DirectX Compute

June 2026:

• Week 1-2: Geo Operations GPU
• Week 3-4: Documentation & Release

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6. Risks & Mitigation

Risk 1: CUDA Version Compatibility

Mitigation: Support CUDA 11.0+, test on multiple GPU generations

Risk 2: VRAM Exhaustion

Mitigation: Chunked processing, VRAM monitoring, automatic CPU fallback

Risk 3: Performance Not Meeting Expectations

Mitigation: Early prototyping, profiling, hybrid CPU/GPU strategy

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7. Success Criteria

Performance:

• ✅ 10x speedup for batch vector search
• ✅ 5x speedup for geo operations
• ✅ Graceful degradation to CPU

Quality:

• ✅ Correctness verified
• ✅ No memory leaks
• ✅ Complete documentation

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Vollständige technische Details: Siehe extended version in repository documentation

Letzte Aktualisierung: 20. November 2025
Version: 1.0
Nächstes Review: Januar 2026