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About optimize-for-gpu
optimize-for-gpu
GPU-accelerates scientific Python on NVIDIA hardware and verifies
optimize-for-gpu is an agent skill for speeding up scientific Python on NVIDIA hardware with GPU acceleration, and for checking that the result is both correct and faster. It applies to CUDA and GPU optimisation of CPU-bound NumPy, SciPy and similar code. The skill treats GPU acceleration as an evidence-driven optimisation, not an automatic rewrite. It preserves the numerical and algorithmic behaviour the user depends on, measures with representative data, and keeps the GPU version only when synchronised end-to-end benchmarks show a useful improvement. The skill was reviewed against RAPIDS 26.08, CuPy 14.2, Numba-CUDA 0.30.4 and Warp 1.17. Its reference files contain illustrative GPU examples, and the review checked the source and the API but did not run them on CUDA hardware. The skill tells you to validate the examples on your own target GPU before you report correctness or a speed-up. It also warns against using a moving, latest documentation page to infer compatibility with a pinned release. Use it when a Python workload is slow on the CPU and you want to know whether a GPU version is worth the effort.
- Price
- Free
- Made by
- k-dense-ai
- Category
- AI Skills
- Website
- github.com/k-dense-ai/scientific-agent-skills/optimize-for-gpu