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Benchmarking OpenCL, OpenACC, OpenMP, and CUDA: Programming Productivity, Performance, and Energy Consumption
Linnaeus University, Faculty of Technology, Department of Computer Science. (Parallel Computing)
Linköping University.
Linnaeus University, Faculty of Technology, Department of Computer Science. (Parallel Computing)
Cracow University of Technology, Poland.
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2017 (English)In: ProceedingARMS-CC '17 Proceedings of the 2017 Workshop on Adaptive Resource Management and Scheduling for Cloud Computing, New York, NY, USA: Association for Computing Machinery (ACM), 2017, 1-6 p.Conference paper, Published paper (Refereed)
Abstract [en]

Many modern parallel computing systems are heterogeneous at their node level. Such nodes may comprise general purpose CPUs and accelerators (such as, GPU, or Intel Xeon Phi) that provide high performance with suitable energy-consumption characteristics. However, exploiting the available performance of heterogeneous architectures may be challenging. There are various parallel programming frameworks (such as, OpenMP, OpenCL, OpenACC, CUDA) and selecting the one that is suitable for a target context is not straightforward. In this paper, we study empirically the characteristics of OpenMP, OpenACC, OpenCL, and CUDA with respect to programming productivity, performance, and energy. To evaluate the programming productivity we use our homegrown tool CodeStat, which enables us to determine the percentage of code lines required to parallelize the code using a specific framework. We use our tools MeterPU and x-MeterPU to evaluate the energy consumption and the performance. Experiments are conducted using the industry-standard SPEC benchmark suite and the Rodinia benchmark suite for accelerated computing on heterogeneous systems that combine Intel Xeon E5 Processors with a GPU accelerator or an Intel Xeon Phi co-processor.

Place, publisher, year, edition, pages
New York, NY, USA: Association for Computing Machinery (ACM), 2017. 1-6 p.
National Category
Computer Systems Computer Science
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-67141DOI: 10.1145/3110355.3110356ISBN: 978-1-4503-5116-4 (print)OAI: oai:DiVA.org:lnu-67141DiVA: diva2:1129227
Conference
ARMS-CC '17: the 2017 Workshop on Adaptive Resource Management and Scheduling for Cloud Computing, 28 July, 2017
Available from: 2017-08-01 Created: 2017-08-01 Last updated: 2017-09-15Bibliographically approved

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Publisher's full texthttp://dl.acm.org/citation.cfm?doid=3110355.3110356

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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