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High-performance GPU programming in a high-level language. JuliaGPU is a GitHub organization created to unify the many packages for programming GPUs in Julia. With its high-level syntax and flexible compiler, Julia is well-positioned to productively program hardware accelerators like GPUs without sacrificing performance. The latest development version of CUDA.jl requires Julia 1.8 or higher. If you are using an older version of Julia, you need to use a previous version of CUDA.jl. This will...

...For example, a 2-D shallow ice solver presented at JuliaCon 2020 [1] achieved a nearly 20 times better performance than a corresponding GPU Array programming implementation; in absolute terms, it reached 70% of the theoretical upper performance bound of the used Nvidia P100 GPU, as defined by the effective throughput metric, T_eff. ParallelStencil relies on the native kernel programming capabilities of CUDA.jl and AMDGPU.jl and on Base.Threads for high-performance computations on GPUs and CPUs, respectively. It is seamlessly interoperable with ImplicitGlobalGrid.jl, which renders the distributed parallelization of stencil-based GPU and CPU apps.

The programming support for NVIDIA GPUs in Julia is provided by the CUDA.jl package. It is built on the CUDA toolkit and aims to be as full-featured and offer the same performance as CUDA C. The toolchain is mature, has been under development since 2014, and can easily be installed on any current version of Julia using the integrated package manager.