JACC.jl

CPU/GPU performance portable layer for Julia

JACC.jl follows a function as a argument approach in combination with the power of Julia's ecosystem for multiple dispatch, GPU access via JuliaGPU back ends, and package extensions since Julia v1.9 . Similar to portable layers like Kokkos, users would pass a size and a function including its arguments to a parallel_for or parallel_reduce function. The overall goal is to write a single source code that can be executed on multiple vendor CPU and GPU parallel programming environments. JACC meant to simplify CPU/GPU kernel programming using a simple application programming interface (API).

Quick start

1. Set a back end: "cuda", "amdgpu", or "threads" (default) with JACC.JACCPreferences generating a LocalPreferences.toml file

   julia> import JACC.JACCPreferences
   julia> JACCPreferences.set_backend("cuda")
   

2. Run a kernel example (see tests directory)

   import JACC
   
   function axpy(i, alpha, x, y)
     if i <= length(x)
       @inbounds x[i] += alpha * y[i]
     end
   end
   
   N = 10
   # Generate random vectors x and y of length N for the interval [0, 100]
   x = round.(rand(Float32, N) * 100)
   y = round.(rand(Float32, N) * 100)
   alpha = 2.5
   
   x_d = JACC.Array(x)
   y_d = JACC.Array(y)
   JACC.parallel_for(N, axpy, alpha, x_d, y_d)
   

Resources

• For an app integration example see the GrayScott.jl JACC branch and the Simulation.jl code for writing kernels with JACC.jl and selecting specific vendor back ends in Julia.
• JuliaCon 2023 presentation video.
• SC24 JACC Best Research Poster Finalist
• SC24 WACCPD presentation and paper
• MiniVATES.jl proxy application repository and SC24 XLOOP best paper using JACC.jl

Citation

If you find JACC.jl useful please cite the following paper:

@INPROCEEDINGS{JACC,
  author={Valero-Lara, Pedro and Godoy, William F and Mankad, Het and Teranishi, Keita and Vetter, Jeffrey S and Blaschke, Johannes and Schanen, Michel},
  booktitle={Proceedings of the SC '24 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis}, 
  title={{JACC: Leveraging HPC Meta-Programming and Performance Portability with the Just-in-Time and LLVM-based Julia Language}}, 
  year={2024},
  volume={},
  number={},
  pages={},
  doi={10.1109/SCW63240.2024.00245}
}

Sponsors:

Funded by the US Department of Energy Advanced Scientific Computing Research (ASCR) projects:

• S4PST and PESO as part of the Next Generation of Scientific Software Technologies (NGSST)
• ASCR Competitive Portfolios for Advanced Scientific Computing Research

Former sponsors:

• ASCR Bluestone X-Stack
• The Exascale Computing Project - PROTEAS-TUNE