---
name: mkurman/optimize-for-gpu
source: https://app.decimal.ai/s/mkurman-optimize-for-gpu@1/SKILL.md
source_sha256: b53f1eeb0b4b
---

|-------------|
| `references/cupy.md` | User has NumPy/SciPy code, or needs array operations on GPU |
| `references/numba.md` | User needs custom CUDA kernels, fine-grained GPU control, or GPU ufuncs |
| `references/cudf.md` | User has pandas code, or needs dataframe operations on GPU |
| `references/cuml.md` | User has scikit-learn code, or needs ML training/inference/preprocessing on GPU |
| `references/cugraph.md` | User has NetworkX code, or needs graph analytics on GPU |
| `references/warp.md` | User needs GPU simulation, spatial computing, mesh/volume queries, differentiable programming, or robotics |
| `references/kvikio.md` | User needs high-performance file IO to/from GPU, GPUDirect Storage, reading S3/HTTP to GPU, or Zarr on GPU |
| `references/cuxfilter.md` | User wants GPU-accelerated interactive dashboards, cross-filtering, or EDA visualization |
| `references/cucim.md` | User has scikit-image code, or needs image processing, digital pathology, or WSI reading on GPU |
| `references/cuvs.md` | User needs vector search, nearest neighbors, similarity search, or RAG retrieval on GPU |
| `references/cuspatial.md` | User has GeoPandas/shapely code, or needs spatial joins, distance calculations, or trajectory analysis on GPU |
| `references/raft.md` | User needs sparse eigensolvers, device memory management, or multi-GPU primitives |

Read the specific reference before writing code — they contain detailed API patterns, optimization techniques, and pitfalls specific to each library.