optimize-for-gpu

出处与授权

本站只收录项目元数据与简介，不对作者正文做转载；完整内容请到上方出处查看。商用前请自行核对原仓库的 LICENSE 与 NOTICE。

⚠ 上游仓库根目录未找到 LICENSE/COPYING 文件，以上许可证名来自第三方登记、未经核实。请到出处自行确认后再商用。

产品介绍

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.

资源信息

许可证信息摘自官方源，可能过时或缺失。商用前请以原仓库的 LICENSE 文件为准。

功能说明

• •When This Skill Applies
• •Choose the Smallest Suitable Layer
• •Optimization Workflow
• •1. Define the contract and baseline
• •2. Check suitability before porting
• •3. Try the least disruptive implementation
• •4. Keep a coherent GPU data path
• •5. Validate semantics before speed

适用平台