ComputeCpp™ Community Edition, Our Free SYCL™ Implementation
ComputeCpp is a SYCL 1.2.1 conformant implementation developed by Codeplay®. Compile SYCL code to a range of different platforms such as Linux® and Windows® and architectures including x86_64 and AArch64.
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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.
Try Our Experimental ASP Release?
If you would like to try out the experimental "ASP" release of ComputeCpp™ Community Edition, please feel free to do so. Please report any issues using the feedback page.
Comparison of Features
Try the SYCL Playground on Tech.io
Visit our SYCL/ComputeCpp Playground on tech.io 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
living.knowledge
WIGNER Fizikai Kutatóközpont
Sokszín? Fizika
ONERA
The French Aerospace Lab
Stellar Group
Shaping a Scalable Future
