A High-Level Programming Approach for Distributed Systems with Accelerators
Michel Steuwer, Philipp Kegel, Sergei Petrovich Gorlatch · Frontiers in artificial intelligence and applications · 2012
Application programming for modern heterogeneous systems which comprise multiple accelerators (multi-core CPUs and GPUs) is complex and error-prone. Popular approaches, like OpenCL and CUDA, are low-level and offer no support for the two most complicated issues: 1) programming multiple GPUs within a stand-alone computer, and 2) managing distributed systems that integrate several such computers. In particular, distributed systems require application developers to use a mix of different programming models, e.g., MPI together with OpenCL or CUDA. We propose a uniform approach based on OpenCL for programming both stand-alone and distributed systems with GPUs. The approach implementation is based on two parts: 1) the SkelCL library for high-level application programming on heterogeneous stand-alone computers with multi-core CPUs and multiple GPUs, and 2) the dOpenCL middleware for transparent execution of OpenCL programs on several stand-alone computers connected over a network.