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oneAPI.jl

Julia support for the oneAPI programming toolkit.

oneAPI.jl provides support for working with the oneAPI unified programming model. The package is currently verified to work with the implementation provided by the Intel Compute Runtime, primarily on Linux.

Writing Portable Code

While oneAPI.jl provides specific functionality for Intel GPUs, it is highly recommended to write backend-agnostic code whenever possible. This allows your code to run on various hardware backends (NVIDIA, AMD, Intel, Apple) without modification.

  • GPUArrays.jl: Use high-level array abstractions that work across different GPU backends.
  • KernelAbstractions.jl: Use this package for writing kernels that can be compiled for CPU, CUDA, ROCm, and oneAPI devices.

Direct use of oneAPI-specific macros (like @oneapi) and types (like oneArray) should be reserved for cases where you need specific optimizations or features not covered by the generic abstractions.

Features

  • High-level Array Abstractions: oneArray type fully implementing the GPUArrays.jl interface.
  • Kernel Programming: Execute custom kernels written in Julia on Intel GPUs, compiled to SPIR-V through LLVM's SPIR-V back-end (or the Khronos SPIR-V translator on Intel's LTS driver stack).
  • KernelAbstractions.jl Backend: The oneAPIBackend back-end runs portable kernels on Intel GPUs unmodified.
  • Level Zero Integration: Low-level access to the Level Zero API via the oneL0 submodule.
  • oneMKL Support: Integration with Intel oneMKL for BLAS, LAPACK, sparse and FFT operations.
  • SYCL Integration: Interoperability with SYCL (on Linux).

Requirements

  • Julia: 1.10 or higher
  • OS: Linux. Windows is experimental, via WSL2.
  • Hardware: Intel Gen9 graphics or newer. Intel Arc GPUs (A580, A750, A770, etc) require Linux 6.2 or newer.