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.
University of Münster
WIGNER Fizikai Kutatóközpont
The French Aerospace Lab
Shaping a Scalable Future
University of the West of Scotland