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A C++ GPU Computing Library for OpenCL
C++ CMake Cuda Other
Latest commit c75f5dc @kylelutz kylelutz Merge pull request #516 from boostorg/adapt-struct-array
Add array support to BOOST_COMPUTE_ADAPT_STRUCT()


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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


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
        host_vector.begin(), host_vector.end(), device_vector.begin(), queue

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

    // copy data back to the host
        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.


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 ( for more information.

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