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.

Reviewed against RAPIDS 26.08, CuPy 14.2, Numba-CUDA 0.30.4, and Warp 1.17. The references contain illustrative GPU examples: source/API review is not execution on CUDA hardware. Validate them on the user's target GPU before reporting correctness or speedup. Do not use a moving latest documentation page to infer compatibility with a pinned release.

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 needs mesh operations (ray casting, closest-point queries, signed distance fields) or geometry processing on GPU
• User is doing robotics (kinematics, dynamics, control) with transforms and quaternions
• User has Python simulation loops that could be JIT-compiled to GPU kernels
• User mentions NVIDIA Warp or wants differentiable GPU simulation integrated with PyTorch/JAX
• User is doing simulations, signal processing, financial modeling, bioinformatics, physics, or any compute-intensive work
• User wants to optimize existing code and GPU acceleration is the right answer

Choose the Smallest Suitable Layer

Prefer a maintained library implementation over a custom kernel:

Do not move code out of PyTorch, JAX, TensorFlow, or another GPU-native framework merely to use one of these libraries. First remove CPU round trips and use the framework's compiler, profiler, mixed-precision, and batching facilities.

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