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A Memory Efficient Parallel All-Pairs Computation Framework: Computation – Communication Overlap

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Parallel Processing and Applied Mathematics (PPAM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10777))

Abstract

All-Pairs problems require each data element in a set of N data elements to be paired with every other data element for specific computation using the two data elements. Our framework aims to address recurring problems of scalability, distributing equal work load to all nodes and by reducing memory footprint. We reduce memory footprint of All-Pairs problems, by reducing memory requirement from \(N/\sqrt{P}\) to 3N/P. A bio-informatics application is implemented to demonstrate the scalability ranging up to 512 cores for the data set we experimented, redundancy management, and speed up performance of the framework.

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Acknowledgements

The research reported in this paper is partially supported by the Philip and Virginia Sproul Professor Endowment and HPC@ISU equipment at Iowa State University, some of which has been purchased through funding provided by NSF under MRI grant number NSF CNS grant number 1229081 and NSF CRI grant number 1205413. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, 50010, USA

    Venkata Kasi Viswanath Yeleswarapu & Arun K. Somani

Authors
  1. Venkata Kasi Viswanath Yeleswarapu
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  2. Arun K. Somani
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Correspondence to Venkata Kasi Viswanath Yeleswarapu .

Editor information

Editors and Affiliations

  1. Czestochowa University of Technology, Czestochowa, Poland

    Roman Wyrzykowski

  2. University of Tennessee, Knoxville, Tennessee, USA

    Jack Dongarra

  3. University of Southern California, Marina Del Rey, California, USA

    Ewa Deelman

  4. Czestochowa University of Technology, Czestochowa, Poland

    Konrad Karczewski

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Yeleswarapu, V.K.V., Somani, A.K. (2018). A Memory Efficient Parallel All-Pairs Computation Framework: Computation – Communication Overlap. In: Wyrzykowski, R., Dongarra, J., Deelman, E., Karczewski, K. (eds) Parallel Processing and Applied Mathematics. PPAM 2017. Lecture Notes in Computer Science(), vol 10777. Springer, Cham. https://doi.org/10.1007/978-3-319-78024-5_39

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  • DOI: https://doi.org/10.1007/978-3-319-78024-5_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78023-8

  • Online ISBN: 978-3-319-78024-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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