optimize-for-gpu
Skill for GPU-accelerating Python code with CuPy, Numba CUDA and RAPIDS libraries.
About
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.
At a glance
- Category
- Developer tools Code intelligence
- Made for
- Developers
- Pricing
- Free
- Access
- No sign-in
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Capabilities
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Provenance
- Discovered
- Relayed by github
- Identifier
- urn:air:github.com:k-dense-ai:scientific-agent-skills:optimize-for-gpu
- Catalog host
- github.com · via github registry
- Anchor check
- Not anchored
- Last crawled
- seen 11d ago
Discovered through github.com's registry, not anchored by StealthStack. We relay the listing as-is; we have not checked that the URN authority matches the publishing host. How trust works →