OpenCL.jl

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.

Support of Julia v0.4

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.

This package is based off the work of others:

PyOpenCL by Andreas Klockner
oclpb by Sean Ross
Boost.Compute by Kyle Lutz
rust-opencl

Contributors

Jake Bolewski (@jakebolewski)
Valentin Churavy (@vchuravy)
Simon Danisch (@SimonDanisch)

Setup

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()
```

IJulia Notebooks

Quick Example

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

yuyichao / OpenCL.jl