KernelAbstractions.jl (KA) is a package that allows you to write GPU-like kernels targetting different execution backends. KA intends to be a minimal and performant library that explores ways to write heterogeneous code. Although parts of the package are still experimental, it has been used successfully as part of the Exascale Computing Project to run Julia code on pre-Frontier and pre-Aurora systems. Currently, profiling and debugging require backend-specific calls like, for example, in CUDA.jl.


While KernelAbstraction.jl is focused on performance portability, it emulates GPU semantics and therefore the kernel language has several constructs that are necessary for good performance on the GPU, but serve no purpose on the CPU. In these cases, we either ignore such statements entirely (such as with @synchronize) or swap out the construct for something similar on the CPU (such as using an MVector to replace @localmem). This means that CPU performance will still be fast, but might be performing extra work to provide a consistent programming model across GPU and CPU

Supported backends

All supported backends rely on their respective Julia interface to the compiler backend and depend on GPUArrays.jl and GPUCompiler.jl.


import CUDA
using KernelAbstractions

CUDA.jl is currently the most mature way to program for GPUs. This provides a backend CUDABackend <: KA.Backend to CUDA.



Major refactor of KernelAbstractions. In particular:

  • Removal of the event system. Kernel are now implicitly ordered.
  • Removal of backend packages, backends are now directly provided by CUDA.jl and similar

Semantic differences


  1. The kernels are automatically bounds-checked against either the dynamic or statically provided ndrange.
  2. Kernels implictly return nothing


Please file any bug reports through Github issues or fixes through a pull request. Any heterogeneous hardware or code aficionados is welcome to join us on our journey.