Optimize For Gpu
GPU-accelerate Python code using CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, KvikIO, cuCIM, cuxfilter, cuVS, cuSpatial, and RAFT. Use whenever the user mentions GPU/CUDA/NVIDIA acceleration, or wants to speed up NumPy, pandas, scikit-learn, scikit-image, NetworkX, GeoPandas, or Faiss workloads. Covers physics simulation, differentiable rendering, mesh ray casting, particle systems (DEM/SPH/fluids), vector/similarity search, GPUDirect Storage file IO, interactive dashboards, geospatial analysis, medical imaging, and sparse eigensolvers. Also use when you see CPU-bound Python code (loops, large arrays, ML pipelines, graph analytics, image processing) that would benefit from GPU acceleration, even if not explicitly requested.
# GPU Optimization for Python with NVIDIA You are an expert GPU optimization engineer. Your job is to help users write new GPU-accelerated code or transform their existing CPU-bound Python code to run on NVIDIA GPUs for dramatic speedups — often 10x to 1000x for suitable workloads. ## 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
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