Numba CUDA
  • User guide
  • Reference documentation
Numba CUDA
  • Numba-CUDA
  • View page source

Numba-CUDA

Numba-CUDA provides a CUDA target for the Numba Python JIT Compiler. It is used for writing SIMT kernels in Python, for providing Python bindings for accelerated device libraries, and as a compiler for user-defined functions in accelerated libraries like RAPIDS.

  • To install Numba-CUDA, see: Installation.

  • To get started writing CUDA kernels in Python with Numba, see Writing CUDA Kernels.

  • Browse the Examples to see a variety of use cases of Numba-CUDA.

Contents

  • User guide
    • Installation
    • Writing CUDA Kernels
    • Memory management
    • Writing Device Functions
    • Supported Python features in CUDA Python
    • CUDA Fast Math
    • Supported Atomic Operations
    • Cooperative Groups
    • Random Number Generation
    • Device management
    • The Device List
    • Device UUIDs
    • Examples
    • Debugging CUDA Python with the the CUDA Simulator
    • GPU Reduction
    • CUDA Ufuncs and Generalized Ufuncs
    • Sharing CUDA Memory
    • CUDA Array Interface (Version 3)
    • External Memory Management (EMM) Plugin interface
    • CUDA Bindings
    • Calling foreign functions from Python kernels
    • Compiling Python functions for use with other languages
    • On-disk Kernel Caching
    • CUDA Minor Version Compatibility
    • CUDA Frequently Asked Questions
  • Reference documentation
    • CUDA Host API
    • CUDA Kernel API
    • CUDA-Specific Types
    • Memory Management
    • Libdevice functions
    • Environment Variables
Next

© Copyright 2012-2024 Anaconda Inc. 2024, NVIDIA Corporation..