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).

JuliaCon 2023 presentation video.

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)

We currently have a limited number of configurations. We hope to study and incoorporate more relevant cases and dimensions shapes as needed. For an app integration example see the GrayScott.jl JACC branch and the Simulation.jl for writing kernels with JACC.jl and selecting specific vendor back ends in Julia.

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)
• Bluestone X-Stack

Past sponsors:

• The Exascale Computing Project (ECP) PROTEAS-TUNE