OpenCL bindings for Julia
Julia interface for the OpenCL parallel computation API
This package aims to be a complete solution for OpenCL programming in Julia, similar in scope to PyOpenCL for Python. It provides a high level api for OpenCL to make programing GPU's and multicore CPU's much less onerous.
OpenCL.jl provides access to OpenCL API versions 1.0, 1.1, 1.2 and 2.0.
Currently OpenCL.jl
only supports Julia v0.3 due to some breaking changes in Julia v0.4. Support is comming as soon as Julia v0.4 is entering its prerelease phase.
- PyOpenCL by Andreas Klockner
- oclpb by Sean Ross
- Boost.Compute by Kyle Lutz
- rust-opencl
- Jake Bolewski (@jakebolewski)
- Valentin Churavy (@vchuravy)
- Simon Danisch (@SimonDanisch)
-
Install an OpenCL driver. If you use OSX, OpenCL is already available
-
Checkout the packages from the Julia repl
Pkg.add("OpenCL")
-
OpenCL will be installed in your
.julia
directory -
cd
into your.julia
directory to run the tests and try out the examples -
To update to the latest development version, from the Julia repl:
Pkg.update()
import OpenCL
const cl = OpenCL
const sum_kernel = "
__kernel void sum(__global const float *a,
__global const float *b,
__global float *c)
{
int gid = get_global_id(0);
c[gid] = a[gid] + b[gid];
}
"
a = rand(Float32, 50_000)
b = rand(Float32, 50_000)
device, ctx, queue = cl.create_compute_context()
a_buff = cl.Buffer(Float32, ctx, (:r, :copy), hostbuf=a)
b_buff = cl.Buffer(Float32, ctx, (:r, :copy), hostbuf=b)
c_buff = cl.Buffer(Float32, ctx, :w, length(a))
p = cl.Program(ctx, source=sum_kernel) |> cl.build!
k = cl.Kernel(p, "sum")
cl.call(queue, k, size(a), nothing, a_buff, b_buff, c_buff)
r = cl.read(queue, c_buff)
if isapprox(norm(r - (a+b)), zero(Float32))
info("Success!")
else
error("Norm should be 0.0f")
end