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Analysis of Partitioning Models and Metrics in Parallel Sparse Matrix-Vector Multiplication

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Parallel Processing and Applied Mathematics (PPAM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8385))

Abstract

Graph/hypergraph partitioning models and methods have been successfully used to minimize the communication among processors in several parallel computing applications. Parallel sparse matrix-vector multiplication (SpMxV) is one of the representative applications that renders these models and methods indispensable in many scientific computing contexts. We investigate the interplay of the partitioning metrics and execution times of SpMxV implementations in three libraries: Trilinos, PETSc, and an in-house one. We carry out experiments with up to 512 processors and investigate the results with regression analysis. Our experiments show that the partitioning metrics influence the performance greatly in a distributed memory setting. The regression analyses demonstrate which metric is the most influential for the execution time of the libraries.

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Author information

Authors and Affiliations

  1. Department of Biomedical Informatics, The Ohio State University, Columbus, USA

    Kamer Kaya & Ümit V. Çatalyürek

  2. CNRS and LIP, ENS Lyon, Lyon, France

    Bora Uçar

  3. Department of Electrical and Computer Engineering, The Ohio State University, Columbus, USA

    Ümit V. Çatalyürek

Authors
  1. Kamer Kaya
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  2. Bora Uçar
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  3. Ümit V. Çatalyürek
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Corresponding author

Correspondence to Bora Uçar .

Editor information

Editors and Affiliations

  1. Institute of Computer and Information Science, Czestochowa University of Technology, Czestochowa, Poland

    Roman Wyrzykowski

  2. University of Tennessee, Department of Computer Science, Knoxville, Tennessee, USA

    Jack Dongarra

  3. Institute of Computer and Information Science, Czestochowa University of Technology, Czestochowa, Poland

    Konrad Karczewski

  4. Technical University of Denmark Informatics and Mathematical Modelling, Kongens Lyngby, Denmark

    Jerzy Waśniewski

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Kaya, K., Uçar, B., Çatalyürek, Ü.V. (2014). Analysis of Partitioning Models and Metrics in Parallel Sparse Matrix-Vector Multiplication. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2013. Lecture Notes in Computer Science(), vol 8385. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55195-6_16

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  • DOI: https://doi.org/10.1007/978-3-642-55195-6_16

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-55194-9

  • Online ISBN: 978-3-642-55195-6

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