SFU-driven transparent approximation acceleration on GPUs

A. Li, S.L. Song, M. Wijtvliet, A. Kumar, H. Corporaal

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

26 Citations (Scopus)


Approximate computing, the technique that sacrifices certain amount of accuracy in exchange for substantial performance boost or power reduction, is one of the most promising solutions to enable power control and performance scaling towards exascale. Although most existing approximation designs target the emerging data-intensive applications that are comparatively more error-tolerable, there is still high demand for the acceleration of traditional scientific applications (e.g., weather and nuclear simulation), which often comprise intensive transcendental function calls and are very sensitive to accuracy loss. To address this challenge, we focus on a very important but long ignored approximation unit on today's commercial GPUs

Original languageEnglish
Title of host publicationProceedings of the 2016 International Conference on Supercomputing, ICS 2016, 1-3 June 2016, Istanbul, Turkey
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)978-1-4503-4361-9
Publication statusPublished - 1 Jun 2016
Event30th International Conference on Supercomputing, ICS 2016 - Istanbul, Turkey
Duration: 1 Jun 20163 Jun 2016


Conference30th International Conference on Supercomputing, ICS 2016


  • Approximate computing
  • GPU
  • Performance/energy/accuracy trade-offs
  • Program transformation
  • Special-function-unit


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