{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/6"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"},{"label":"Physics","url":"https://skillfed.io/packages/category/scientific-engineering-physics"}],"enrichment":{"capability":"PyOpenCL provides Python bindings to OpenCL, enabling you to write GPU and massively parallel compute code that runs on diverse hardware accelerators through a Pythonic interface.","skillfed_tags":["gpu-computing","parallel-processing","heterogeneous-compute"],"use_cases":["Accelerate numerical computations (linear algebra, FFTs, simulations) by offloading to GPU","Implement custom compute kernels for image processing or signal processing on parallel hardware","Prototype heterogeneous computing workflows that mix CPU and GPU workloads in a single Python script","Deploy machine learning inference on embedded or mobile GPUs via OpenCL","Run scientific simulations (physics, chemistry) that benefit from massively parallel execution"],"what_it_does":"PyOpenCL is a Python wrapper around the OpenCL API that lets you write code to run on GPUs and other massively parallel compute devices. It follows the design philosophy of its sister project PyCUDA, offering automatic resource cleanup via RAII patterns, complete access to OpenCL's API surface, automatic error translation to Python exceptions, and a C++-based implementation for performance. The package supports Apple, AMD, and Nvidia OpenCL implementations and is actively maintained with broad platform coverage.\n\nTypical use involves creating an OpenCL context tied to available compute devices, building or loading kernels written in OpenCL C, and managing data transfer between host and device memory. The runtime dependencies (numpy, pytools, platformdirs, importlib-resources, typing_extensions) support array handling, platform detection, and utility functions. Installation via conda or pre-built wheels is recommended to avoid building from source, which requires a C++17 compiler and OpenCL headers.","worth_installing":"Yes, if you need GPU acceleration and want to target diverse hardware via OpenCL. The package is production-stable, actively maintained, permissively licensed, and widely used. Install friction is moderate due to compiled dependencies and OpenCL driver requirements, but pre-built wheels and conda packages ease setup. No known security vulnerabilities. Not necessary if you are locked into a single vendor (CUDA-only or Metal-only) or do not need GPU compute."},"id":"pyopencl","links":{"html":"https://skillfed.io/packages/pyopencl","md":"https://skillfed.io/packages/pyopencl.md","pypi":"https://pypi.org/project/pyopencl/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-01-16","license_spdx":"MIT","license_treatment":"permissive","name":"pyopencl","python_support":"supports_current","summary":"Python wrapper for OpenCL"},"popularity":{"monthly_downloads":175184,"position":10268,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2026.1.2"}