The Journal of Supercomputing

, Volume 69, Issue 1, pp 25–33 | Cite as

SkelCL: a high-level extension of OpenCL for multi-GPU systems

Article

Abstract

Application development for modern high-performance systems with graphics processing units (GPUs) currently relies on low-level programming approaches like CUDA and OpenCL, which leads to complex, lengthy and error-prone programs. We present SkelCL—a high-level programming approach for systems with multiple GPUs and its implementation as a library on top of OpenCL. SkelCL makes three main enhancements to the OpenCL standard: (1) memory management is simplified using parallel container data types (vectors and matrices); (2) an automatic data (re)distribution mechanism allows for implicit data movements between GPUs and ensures scalability when using multiple GPUs; (3) computations are conveniently expressed using parallel algorithmic patterns (skeletons). We demonstrate how SkelCL is used to implement parallel applications, and we report experimental evaluation of our approach in terms of programming effort and performance.

Keywords

Parallel programming GPU programming OpenCL Algorithmic skeletons SkelCL Many-cores 

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.University of MuensterMünsterGermany

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