# GPU Acceleration Plan - ThemisDB

**Version:** 1.0  
**Datum:** 20. November 2025  
**Status:** Planning Phase  
**Priorität:** P0 (Q2 2026)

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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