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
  • Global Variables and Captured Values
  • 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 Numba CUDA Programs with Visual Studio Code and CUDA GDB
  • 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
  • CUDA device call conventions
  • On-disk Kernel Caching
  • CUDA Minor Version Compatibility
  • CUDA Frequently Asked Questions
• Reference documentation
  • CUDA Host API
  • CUDA Kernel API
  • Cache Hints for Memory Operations
  • CUDA-Specific Types
  • Memory Management
  • Libdevice functions
  • Environment Variables