Agent skill · software engineering · k-dense-ai
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.
Why this skill is useful
Adds executable scripts and specific GPU optimization patterns for Python that the AI wouldn't reliably generate on its own.
What it needs
Requires cudf, cugraph, cuml, cupy, cuxfilter, kvikio, numba, warp installed locally. About 7k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
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) User is doing graph analytics (centrality, community detection, shortest paths, PageRank, etc.) User is doing vector search, nearest neighbor search, similarity search, or building a RAG pipeline User has Faiss, Annoy, ScaNN, or sklearn NearestNeighbors code that could be GPU-accelerated User wants GPU-accelerated interactive dashboards, cross-filtering, or exploratory data analysis on large datasets User is doing geospatial analysis (point-in-polygon, spatial joins, trajectory analysis, distance calculations) with GeoPandas or shapely User is doing image processing, computer vision, or medical imaging (filtering, segmentation, morphology, feature detection) with scikit-image or OpenCV User is working with whole-slide images (WSI), digital pathology, microscopy, or remote sensing imagery User is loading large binary data files into GPU memory (numpy.fromfile → cupy, or Python open() → GPU array) User needs to read files from S3, HTTP, or WebHDFS directly into GPU memory User mentions GPUDirect Storage (GDS) or wants to bypass CPU-memory staging for file IO User is doing physics simulation (particles, cloth, fluids, rigid bodies) or differentiable simulation User …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/optimize-for-gpu