Boost.Compute alternatives and similar libraries
Based on the "Concurrency" category.
Alternatively, view Boost.Compute alternatives based on common mentions on social networks and blogs.
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Thrust
A parallel algorithms library which resembles the C++ Standard Template Library (STL). [Apache2] -
C++ Actor Framework
An Open Source Implementation of the Actor Model in C++. [BSD-3-Clause] -
readerwriterqueue
A fast single-producer, single-consumer lock-free queue for C++. [BSD] -
HPX
A general purpose C++ runtime system for parallel and distributed applications of any scale. [Boost] -
ck
Concurrency primitives, safe memory reclamation mechanisms and non-blocking data structures. [BSD] -
moderngpu
moderngpu is a productivity library for general-purpose computing on GPUs. It is a header-only C++ library written for CUDA. The unique value of the library is in its accelerated primitives for solving irregularly parallel problems. [FreeBSD & Copyright, Sean Baxter] -
continuable
Async C++14 platform independent continuation chainer providing light and allocation aware futures -
A C++14 library for executors
C++ library for executors -
SPSCQueue.h
A bounded single-producer single-consumer wait-free and lock-free queue written in C++11 -
Easy Creation of GnuPlot Scripts from C++
A simple C++17 library to quickly plot your data with GnuPlot -
CUB
CUB provides state-of-the-art, reusable software components for every layer of the CUDA programming mode. [New BSD] -
BlockingCollection
C++11 thread safe collection class modeled after .NET BlockingCollection -
eXtended Template Library
eXtended Template Library -
OpenCL
The open standard for parallel programming of heterogeneous systems.
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README
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:
#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 ([email protected]) for more information.