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International Workshop on Languages and Compilers for Parallel Computing

LCPC 2016: Languages and Compilers for Parallel Computing pp 88–92Cite as

Harnessing Parallelism in Multicore Systems to Expedite and Improve Function Approximation

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10136))

Abstract

Approximating functions in applications that can tolerate some inaccuracy in their results can deliver substantial performance gains. This paper makes a case for harnessing available parallelism in multicore systems to improve performance as well as the quality of function approximation. To that end, we discuss a number of tasks that the function approximation schemes can offload to available parallel cores. We also discuss how leveraging parallelism can help provide guarantees about results and dynamically improve approximations. Finally, we present experimental results of a function approximation scheme.

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References

  1. Aurangzeb, Eigenmann, R.: History-based piecewise approximation scheme for procedures. In: 2nd Workshop on Approximate Computing (WAPCO), January 2016

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  2. Czakon, M., Mitov, A.: Top++. http://www.alexandermitov.com/software/115-top-versions-and-downloads

  3. Samadi, M., Jamshidi, D.A., Lee, J., Mahlke, S.: Paraprox: pattern-based approximation for data parallel applications. ACM SIGARCH Comput. Archit. News 42, 35–50 (2014)

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Author information

Authors and Affiliations

  1. Purdue University, West Lafayette, USA

    Aurangzeb & Rudolf Eigenmann

Authors
  1. Aurangzeb
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  2. Rudolf Eigenmann
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Corresponding author

Correspondence to Aurangzeb .

Editor information

Editors and Affiliations

  1. University of Rochester , Rochester, New York, USA

    Chen Ding

  2. University of Rochester , Rochester, New York, USA

    John Criswell

  3. Huawei Inc. , Santa Clara, California, USA

    Peng Wu

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© 2017 Springer International Publishing AG

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Cite this paper

Aurangzeb, Eigenmann, R. (2017). Harnessing Parallelism in Multicore Systems to Expedite and Improve Function Approximation. In: Ding, C., Criswell, J., Wu, P. (eds) Languages and Compilers for Parallel Computing. LCPC 2016. Lecture Notes in Computer Science(), vol 10136. Springer, Cham. https://doi.org/10.1007/978-3-319-52709-3_7

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  • DOI: https://doi.org/10.1007/978-3-319-52709-3_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52708-6

  • Online ISBN: 978-3-319-52709-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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