CUDA-on-CL
Hugh D. Perkins · 2017
In the machine learning domain, machine learning frameworks are predominantly written and maintained in NVIDIA® CUDA™ language. There have been attempts to port these frameworks to OpenCL®, notably the ports of Caffe framework by Gu et al; Tschopp; and Engel; and of Torch framework by Perkins. The authors of these frameworks found merging their work into the mainstream framework challenging, and maintain their forks as separate branches or repositories. CUDA-on-CL addresses this problem by leaving the reference implementation entirely in NVIDIA CUDA, both host-side and device-side, and providing a compiler and a runtime component, so that any CUDA C++11 application can in theory be compiled and run on any OpenCL 1.2 device. We use Tensorflow framework as a case-study, and demonstrate the ability to run unary, binary and reduction Tensorflow and Eigen kernels, with no modification to the original CUDA source-code.