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.
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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 FeaturesFeatures
ComputeCpp Community Edition
ComputeCpp Professional Edition
SYCL 1.2.1
x86 and ARM binaries
Cross-compilation
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 Tech.ioVisit our SYCL/ComputeCpp Playground on tech.io and build up hands on experience with the SYCL specification.
Who is ComputeCpp for? Portable Parallel Computing ApplicationsOpenCL 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 ComputeCppMachine 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 ApplicationsPerforming 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 ApplicationsThe 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.
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