Optimize for GPU
技能 已验证 活跃GPU-accelerate Python code using CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, KvikIO, cuCIM, cuxfilter, cuVS, cuSpatial, and RAFT. Use whenever the user mentions GPU/CUDA/NVIDIA acceleration, or wants to speed up NumPy, pandas, scikit-learn, scikit-image, NetworkX, GeoPandas, or Faiss workloads. Covers physics simulation, differentiable rendering, mesh ray casting, particle systems (DEM/SPH/fluids), vector/similarity search, GPUDirect Storage file IO, interactive dashboards, geospatial analysis, medical imaging, and sparse eigensolvers. Also use when you see CPU-bound Python code (loops, large arrays, ML pipelines, graph analytics, image processing) that would benefit from GPU acceleration, even if not explicitly requested.
To guide users on selecting and effectively using a wide range of GPU-accelerated libraries for Python scientific computing tasks.
功能
- GPU-accelerates NumPy, pandas, scikit-learn, and other scientific workloads
- Provides detailed guidance on CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, KvikIO, cuCIM, cuxfilter, cuVS, cuSpatial, and RAFT
- Covers physics simulation, ML, graph analytics, geospatial analysis, image processing, and file IO
- Offers installation instructions and performance optimization tips
- Includes code examples for common transformation patterns
使用场景
- Speeding up CPU-bound Python loops and array operations
- Migrating existing pandas or NumPy code to GPU
- Implementing custom CUDA kernels for specific algorithms
- Performing large-scale graph analytics or machine learning on GPU
- Optimizing data loading and preprocessing for GPU pipelines
非目标
- Providing direct execution of GPU code
- Offering a single monolithic GPU acceleration tool
- Covering non-NVIDIA GPU hardware
安装
npx skills add K-Dense-AI/claude-scientific-skills通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
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