Individual skill
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.
backend/cli/skills/coding/optimize-for-gpu/SKILL.md
Source commit a08e0ad0ad3d6448123039ee4659163eb2e35d99
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- Last checked
- Nov 2, 2026
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- 4,355
- Repository last updated
- Nov 7, 2026
- License
- Apache-2.0
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SKILL.mdcore · 13,058 bytesSKILL.mdcore · 13,058 bytesreferences/code_transformation_patterns.mddoc · 9,427 bytesreferences/cucim.mddoc · 20,763 bytesreferences/cudf.mddoc · 20,498 bytesreferences/cugraph.mddoc · 27,267 bytesreferences/cuml.mddoc · 23,794 bytesreferences/cupy.mddoc · 21,579 bytesreferences/cuspatial.mddoc · 14,526 bytesreferences/cuvs.mddoc · 20,783 bytesreferences/cuxfilter.mddoc · 18,774 bytesreferences/decision_framework.mddoc · 15,934 bytesreferences/installation.mddoc · 4,819 bytesreferences/kvikio.mddoc · 17,495 bytesreferences/numba.mddoc · 25,985 bytesreferences/raft.mddoc · 11,308 bytesreferences/warp.mddoc · 19,641 bytes
Read excerpt
# GPU Optimization for Python with NVIDIA
Treat GPU acceleration as an evidence-driven optimization, not an automatic rewrite. Preserve the
user's numerical and algorithmic contract, measure with representative data, and keep the GPU
version only when synchronized end-to-end benchmarks show a useful improvement.
## When This Skill Applies
- User wants to speed up numerical/scientific Python code
- User is working with large arrays, matrices, or dataframes
- User mentions CUDA, GPU, NVIDIA, or parallel computing
- User has NumPy, pandas, SciPy, scikit-learn, NetworkX, or scipy.sparse.linalg code that processes large datasets
- User needs low-level GPU primitives (sparse eigensolvers, device memory management, multi-GPU communication)
- User is doing machine learning (training, inference, hyperparameter tuning, preprocessing)