ComputeCpp™ Community Edition

Download the latest Community Edition of ComputeCpp and browse the developer guides.

  • Accelerate Your Application with ComputeCpp

    ComputeCpp, Codeplay's implementation of the open standard SYCL, enables you to integrate parallel computing into your application and accelerate your code across OpenCL devices such as GPUs. Applications that require a large number of common operations can make huge performance improvements by running the operations in parallel on OpenCL devices. For example, the neural networks used in machine learning perform huge numbers of matrix calculations and ComputeCpp can be used to run these operations in parallel, vastly increasing performance and reducing the power consumption of the application.

    With ComputeCpp and SYCL you can write code once and execute on a range of OpenCL enabled devices reducing your development effort. Develop with standard C++ and the SYCL open standard, re-using your existing C++ libraries. ComputeCpp is also building support for C++17 Parallel STL enabling parallelized library functions to run on accelerated processors. ComputeCpp works with a number of frameworks including SYCL-DNN, ParallelSTL and VisionCpp.

Comparison of Features

ComputeCpp Community Edition
ComputeCpp Professional Edition
SYCL 1.2
x86 and ARM binaries
Offline kernel compilation
Program execution tracing
Forum-based developer support
Helpdesk developer support
Commercial use license
Kernel Performance Tool Inspector
Multi-Binary Support

Try the SYCL Playground on

Visit our SYCL/ComputeCpp Playground on and build up hands on experience with the SYCL specification.

Who is ComputeCpp for?

  • Portable Parallel Computing Applications

    OpenCL devices such as GPUs can be used to accelerate applications by running operations in parallel. By implementing ComputeCpp using the SYCL open standard, developers can write software with C++ single source and run their code using parallel computing across a range of OpenCL devices.

  • Using TensorFlow with ComputeCpp

    Machine learning framework TensorFlow requires large amounts of vector and matrix operations. Performance and power consumption can be vastly improved by using parallel computing. ComputeCpp enables developers to target OpenCL devices such as GPUs using modern C++ code.

  • Artificial Intelligence Applications

    Performing complex image processing operations can be accelerated using parallel computing. ComputeCpp enables high-level programmability for custom vision processors, enabling additional custom features on top of existing optimized hardware functions.

  • Complex Mathematical Applications

    The Eigen library is one of the most popular C++ libraries for linear algebra, matrix and vector operations and related algorithms. Eigen is integrated with ComputeCpp enabling developers to run these operations on OpenCL devices. By taking advantage of these parallel architectures, applications can be accelerated.

Who is Using ComputeCpp?

  • University of Münster


  • WIGNER Fizikai Kutatóközpont

    Sokszín? Fizika


    The French Aerospace Lab

  • Stellar Group

    Shaping a Scalable Future

  • UWS

    University of the West of Scotland

Select a Product

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ComputeCpp enables developers to integrate parallel computing into applications using SYCL and accelerate code on a wide range of OpenCL devices such as GPUs.

ComputeSuite for R-Car enables developers to accelerate their applications on Renesas R-Car based hardware such as the V3M and V3H, using the widely supported open standards SYCL and OpenCL.


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