optimize-for-gpu
GPU-accelerates scientific Python on NVIDIA hardware and verifies that the result is correct and faster. Use for CUDA/GPU optimization; CPU-bound NumPy, SciPy, pandas, scikit-learn, NetworkX, scikit-image, vector-search, image-processing, graph, simulation, or file-I/O workloads; CuPy, cuDF, cuML, cuGraph, cuVS, cuCIM, KvikIO, Warp, Newton, Numba-CUDA, or RAFT questions; and profiling, memory-transfer, kernel, or multi-GPU bottlenecks. Also use when large data-parallel Python code is slow and GPU acceleration is a plausible option, even if the user does not name CUDA.
- Version
- 1.4
- License
- MIT
- Compatibility
- Requires an NVIDIA CUDA-capable GPU for GPU execution. RAPIDS 26.06 requires Python 3.11+ on Linux or WSL2 and matching CUDA 12 or 13 wheels. Package installation needs network access.
Pinned to revision 70a605012eee, so it is the text this page describes rather than whatever the author pushed since.
Files
- skills/optimize-for-gpu/SKILL.md
- skills/optimize-for-gpu/references/code_transformation_patterns.md
- skills/optimize-for-gpu/references/cucim.md
- skills/optimize-for-gpu/references/cudf.md
- skills/optimize-for-gpu/references/cugraph.md
- skills/optimize-for-gpu/references/cuml.md
- skills/optimize-for-gpu/references/cupy.md
- skills/optimize-for-gpu/references/cuspatial.md
- skills/optimize-for-gpu/references/cuvs.md
- skills/optimize-for-gpu/references/cuxfilter.md
- skills/optimize-for-gpu/references/decision_framework.md
- skills/optimize-for-gpu/references/installation.md
- skills/optimize-for-gpu/references/kvikio.md
- skills/optimize-for-gpu/references/numba.md
- skills/optimize-for-gpu/references/raft.md
- skills/optimize-for-gpu/references/warp.md
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