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OpenCL.jl

OpenCL bindings for Julia

Build Status Coverage Status

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.

This package is based off the work of others:

OpenCL.jl has had contributions from many developers.

Setup

  1. Install an OpenCL driver. If you use OSX, OpenCL is already available

  2. Checkout the packages from the Julia repl

    Pkg.add("OpenCL")
  3. OpenCL will be installed in your .julia directory

  4. cd into your .julia directory to run the tests and try out the examples

  5. 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

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OpenCL Julia bindings

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