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

  • Venkata Kasi Viswanath YeleswarapuEmail author
  • Arun K. Somani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Communication - computation overlap High performance computing All-Pairs problems Parallel computing MPI 

Notes

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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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Venkata Kasi Viswanath Yeleswarapu
    • 1
    Email author
  • Arun K. Somani
    • 1
  1. 1.Department of Electrical and Computer EngineeringIowa State UniversityAmesUSA

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