Parallel computing

Julia supports different categories of parallel programming natively:

• Asynchronous "tasks", or coroutines
• Multi-threading
• Distributed computing

Kinetic integrates the the latter two mechanism along with the CUDA-based GPU computing. An initialization function is built in Kinetic.

function __init__()
    np = nworkers()
    nt = Threads.nthreads()
    if nt > 1 || np > 1
        @info "Kinetic will run with $np processors and $nt threads"
    else
        @info "Kinetic will run serially"
    end

    if has_cuda()
        @info "Kinetic will run with CUDA"
        for (i, dev) in enumerate(CUDA.devices())
            @info "$i: $(CUDA.name(dev))"
        end
        @info "Scalar operation is disabled in CUDA"
        CUDA.allowscalar(false)
    end
end

As the package is imported, it will report the computational resources (processors, threads and CUDA devices) that are going to be utilized.