M.I.M.I.R - Multi-agent Intelligent Memory & Insight Repository

// Package opencl provides cross-platform GPU acceleration using OpenCL. // // This package implements GPU-accelerated vector similarity search using // OpenCL, which provides cross-platform support for AMD, Intel, and NVIDIA GPUs. // // # Requirements // // For AMD GPUs on Linux: // - ROCm (Radeon Open Compute): https://rocm.docs.amd.com/ // - Or AMD GPU drivers with OpenCL support // // For AMD GPUs on Windows: // - AMD Adrenalin drivers with OpenCL support // // For Intel GPUs: // - Intel oneAPI or Intel OpenCL runtime // // For NVIDIA GPUs (alternative to CUDA): // - NVIDIA drivers with OpenCL support // // # Build Tags // // This package is only compiled when the "opencl" build tag is present: // // go build -tags opencl // // # Environment Variables // // Linux (AMD ROCm): // // export LD_LIBRARY_PATH=/opt/rocm/opencl/lib:$LD_LIBRARY_PATH // // macOS (via Homebrew): // // Note: macOS deprecated OpenCL in favor of Metal. Use Metal backend instead. // // Windows: // // OpenCL drivers are typically included with GPU drivers. // // # Architecture // // The OpenCL backend uses: // - OpenCL 1.2 or later for compute operations // - Work groups optimized for GPU wavefront/warp sizes // - Coalesced memory access patterns for maximum bandwidth // - Local memory for reduction operations (top-k) // // # Performance Considerations // // OpenCL performance varies by vendor and driver: // - AMD GPUs: Excellent performance with ROCm drivers // - Intel GPUs: Good for integrated graphics, limited VRAM // - NVIDIA: Prefer CUDA for best performance on NVIDIA hardware // // Vector operations are parallelized across compute units: // - Cosine similarity: One work item per vector element // - Top-K search: Parallel reduction with local memory // - Normalization: Per-vector parallelization // // # Example // // Basic usage: // // device, err := opencl.NewDevice(0) // First OpenCL device // if err != nil { // log.Fatal(err) // } // defer device.Release() // // // Create buffer with embeddings // buffer, err := device.NewBuffer(embeddings) // if err != nil { // log.Fatal(err) // } // defer buffer.Release() // // // Search for similar vectors // results, err := device.Search(buffer, query, numVectors, dimensions, topK, normalized) // if err != nil { // log.Fatal(err) // } // // for _, r := range results { // fmt.Printf("Index: %d, Score: %.4f\n", r.Index, r.Score) // } package opencl