The performance power of GPUs could be exposed to applications using two principal kinds of programming interfaces: with manual parallel programming (CUDA or OpenCL), or with directive-based extensions relying on compiler’s capabilities of semi-automatic parallelization (OpenACC and OpenMP4). Unlike for GPUs, Intel has never offered an explicit CUDA-like interface for their Xeon Phi accelerators to general public, leaving OpenMP offloading directives as the only programming option.
Based on liboffloadmic, we have prototyped “micrt” - a programming interface to execute memory transfers and kernels, similarly to CUDA runtime. Find the code example and building instructions here.
Applied Parallel Computing LLC
If you need help with Machine Learning, Computer Vision or with GPU computing in general, please reach out to us at Applied Parallel Computing LLC.