Towards High-Level Programming of Multi-GPU Systems Using the SkelCL Library

Michel Steuwer, Philipp Kegel, Sergei Gorlatch

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Application programming for GPUs (Graphics Processing Units) is complex and error-prone, because the popular approaches - CUDA and OpenCL - are intrinsically low-level and offer no special support for systems consisting of multiple GPUs. The SkelCL library presented in this paper is built on top of the OpenCL standard and offers pre-implemented recurring computation and communication patterns (skeletons) which greatly simplify programming for multi-GPU systems. The library also provides an abstract vector data type and a high-level data (re)distribution mechanism to shield the programmer from the low-level data transfers between the system's main memory and multiple GPUs. In this paper, we focus on the specific support in SkelCL for systems with multiple GPUs and use a real-world application study from the area of medical imaging to demonstrate the reduced programming effort and competitive performance of SkelCL as compared to OpenCL and CUDA. Besides, we illustrate how SkelCL adapts to large-scale, distributed heterogeneous systems in order to simplify their programming.
Original languageEnglish
Title of host publicationProceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops PhD Forum
Place of PublicationWashington, DC, USA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1858-1865
Number of pages8
ISBN (Print)978-0-7695-4676-6
DOIs
Publication statusPublished - 2012

Publication series

NameIPDPSW '12
PublisherIEEE Computer Society

Keywords

  • GPU Computing, GPU Programming, Multi-GPU Systems, SkelCL, OpenCL, Algorithmic Skeletons

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