Skip to main content
SpringerLink
Log in

Scalable Parallel Generation of Very Large Sparse Benchmark Matrices

  • Conference paper
  • First Online:
Parallel Processing and Applied Mathematics (PPAM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8384))

Abstract

We present a method and an accompanying algorithm for scalable parallel generation of sparse matrices intended primarily for benchmarking purposes, namely for evaluation of performance and scalability of generic massively parallel algorithms that involve sparse matrices. The proposed method is based on enlargement of small input matrices, which are supposed to be obtained from public sparse matrix collections containing numerous matrices arising in different application domains and thus having different structural and numerical properties. The resulting matrices are distributed among processors of a parallel computer system. The enlargement process is designed so its users may easily control structural and numerical properties of resulting matrices as well as the distribution of their nonzero elements to particular processors.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Balay, S., Brown, J., Buschelman, K., Eijkhout, V., Gropp, W.D., Kaushik, D., Knepley, M.G., McInnes, L.C., Smith, B.F., Zhang, H.: PETSc users manual. Technical report ANL-95/11 - Revision 3.2, Argonne National Laboratory (2010)

    Google Scholar 

  2. Boisvert, R.F., Pozo, R., Remington, K.: The matrix market exchange formats: initial design. Technical report NISTIR 5935, National Institute of Standards and Technology (1996)

    Google Scholar 

  3. Boisvert, R.F., Pozo, R., Remington, K., Barrett, R.F., Dongarra, J.J.: Matrix market: a web resource for test matrix collections. In: Boisvert, R.F. (ed.) The Quality of Numerical Software: Assessment and Enhancement, pp. 125–137. Chapman & Hall, London (1997)

    Google Scholar 

  4. Davis, T.A., Hu, Y.F.: The University of Florida sparse matrix collection. ACM Trans. Math. Softw. 38(1), 1:1–1:25 (2011)

    MathSciNet  Google Scholar 

  5. Duff, I., Grimes, R., Lewi, J.: User’s guide for the Harwell-Boeing sparse matrix collection (Release I). Technical report TR/PA/92/86, CERFACS. http://people.sc.fsu.edu/~jburkardt/pdf/hbsmc.pdf (1992). Accessed 27 March 2011

  6. Hernandez, V., Roman, J.E., Vidal, V.: SLEPc: a scalable and flexible toolkit for the solution of eigenvalue problems. ACM Trans. Math. Software 31(3), 351–362 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  7. Heroux, M., Bartlett, R., Hoekstra, V.H.R., Hu, J., Kolda, T., Lehoucq, R., Long, K., Pawlowski, R., Phipps, E., Salinger, A., Thornquist, H., Tuminaro, R., Willenbring, J., Williams, A.: An overview of trilinos. Technical report SAND2003-2927, Sandia National Laboratories (2003)

    Google Scholar 

  8. Hoemmen, M.: Matlab (ASCII) sparse matrix format, berkeley Benchmarking and Optimization Group. http://bebop.cs.berkeley.edu/smc/formats/matlab.html (2008). Accessed 27 April 2011

  9. Laub, A.J.: Matrix Analysis for Scientists and Engineers. SIAM, Philadelphia (2005)

    Book  MATH  Google Scholar 

  10. Saad, Y.: Iterative Methods for Sparse Linear Systems, 2nd edn. Society for Industrial and Applied Mathematics, Philadelphia (2003)

    Book  MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by the Czech Science Foundation under Grant No. P202/12/2011, by the U.S. National Science Foundation under Grant No. OCI-0904874, and by the U.S. Department of Energy under Grant No. DOE-0904874. D.L. acknowledges support from Jerry P. Draayer and the Louisiana State University (LSU). We acknowledge the Louisiana Optical Network Initiative (LONI) for providing HPC resources. This research is part of the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (award number OCI 07-25070) and the state of Illinois. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications.

Author information

Authors and Affiliations

  1. Department of Computer Systems, Faculty of Information Technology, Czech Technical University in Prague, Thákurova 9, 160 00, Praha, Czech Republic

    Daniel Langr, Ivan Šimeček & Pavel Tvrdík

  2. Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA, 70803, USA

    Tomáš Dytrych

Authors
  1. Daniel Langr
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ivan Šimeček
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pavel Tvrdík
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tomáš Dytrych
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel Langr .

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

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Langr, D., Šimeček, I., Tvrdík, P., Dytrych, T. (2014). Scalable Parallel Generation of Very Large Sparse Benchmark Matrices. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2013. Lecture Notes in Computer Science(), vol 8384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55224-3_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-55224-3_18

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-55223-6

  • Online ISBN: 978-3-642-55224-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation