Boost.Compute

Boost.Compute is a GPU/parallel-computing library for C++ based on OpenCL.

The core library is a thin C++ wrapper over the OpenCL C API and provides access to compute devices, contexts, command queues and memory buffers.

On top of the core library is a generic, STL-like interface providing common algorithms (e.g. transform(), accumulate(), sort()) along with common containers (e.g. vector<T>, flat_set<T>). It also features a number of extensions including parallel-computing algorithms (e.g. exclusive_scan(), scatter(), reduce()) and a number of fancy iterators (e.g. transform_iterator<>, permutation_iterator<>, zip_iterator<>).

The full documentation is available at http://kylelutz.github.io/compute/.

Example

The following example shows how to sort a vector of floats on the GPU:

#include <vector>
#include <algorithm>
#include <boost/compute.hpp>

namespace compute = boost::compute;

int main()
{
    // get the default compute device
    compute::device gpu = compute::system::default_device();

    // create a compute context and command queue
    compute::context ctx(gpu);
    compute::command_queue queue(ctx, gpu);

    // generate random numbers on the host
    std::vector<float> host_vector(1000000);
    std::generate(host_vector.begin(), host_vector.end(), rand);

    // create vector on the device
    compute::vector<float> device_vector(1000000, ctx);

    // copy data to the device
    compute::copy(
        host_vector.begin(),
        host_vector.end(),
        device_vector.begin(),
        queue
    );

    // sort data on the device
    compute::sort(
        device_vector.begin(),
        device_vector.end(),
        queue
    );

    // copy data back to the host
    compute::copy(
        device_vector.begin(),
        device_vector.end(),
        host_vector.begin(),
        queue
    );

    return 0;
}

Boost.Compute is a header-only library, so no linking is required. The example above can be compiled with:

g++ -I/path/to/compute/include sort.cpp -lOpenCL

More examples can be found in the tutorial.

Support

Bugs and feature requests can be reported through the issue tracker.

Also feel free to send me an email with any problems, questions, or feedback.