Using Different Backends

For any of the examples here, simply use a different GPU array and AcceleratedKernels.jl will pick the right backend:

# Intel Graphics
using oneAPI
v = oneArray{Int32}(undef, 100_000)             # Empty array

# AMD ROCm
using AMDGPU
v = ROCArray{Float64}(1:100_000)                # A range converted to Float64

# Apple Metal
using Metal
v = MtlArray(rand(Float32, 100_000))            # Transfer from host to device

# NVidia CUDA
using CUDA
v = CuArray{UInt32}(0:5:100_000)                # Range with explicit step size

# Transfer GPU array back
v_host = Array(v)

All publicly-exposed functions have CPU implementations with unified parameter interfaces:

import AcceleratedKernels as AK
v = Vector(-1000:1000)                          # Normal CPU array
AK.reduce(+, v, max_tasks=Threads.nthreads())

Note the reduce and mapreduce CPU implementations forward arguments to OhMyThreads.jl, an excellent package for multithreading. The focus of AcceleratedKernels.jl is to provide a unified interface to high-performance implementations of common algorithmic kernels, for both CPUs and GPUs - if you need fine-grained control over threads, scheduling, communication for specialised algorithms (e.g. with highly unequal workloads), consider using OhMyThreads.jl or KernelAbstractions.jl directly.

There is ongoing work on multithreaded CPU sort and accumulate implementations - at the moment, they fall back to single-threaded algorithms; the rest of the library is fully parallelised for both CPUs and GPUs.