A C++ GPU Computing Library for OpenCL

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 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://boostorg.github.io/compute/.

Example

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

C
#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 and under the
examples directory.

Support

Questions about the library (both usage and development) can be posted to the
mailing list.

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.

Help Wanted

The Boost.Compute project is currently looking for additional developers with
interest in parallel computing.

Please send an email to Kyle Lutz (kyle.r.lutz@gmail.com) for more information.

Source: https://github.com/boostorg/compute