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International Symposium on Solving Irregularly Structured Problems in Parallel

IRREGULAR 1997: Solving Irregularly Structured Problems in Parallel pp 198–206Cite as

Performance optimization of combined variable-cost computations and I/O

  • Systems and Applications
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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1253))

Abstract

For applications involving large data sets yielding variablecost computations, achieving both efficient I/O and load balancing may become particularly challenging though performance-critical tasks. In this work, we introduce a data scheduling approach that integrates several optimizing techniques, including dynamic allocation, prefetching, and asynchronous I/O and communications. We show that good scalability is obtained by both hiding the I/O latency and appropriately balancing the workloads. We use a statistical metric for data skewness to further improve the performance by adequately selecting among data-scheduling. We test our approach on sparse benchmark matrices for matrix-vector computations and show experimentally that our method can accurately predict the relative performance of different input/output schemes for a given data set and choose the best technique accordingly.

Author currently on leave of absence from the Department of Control and Computers, the Polytechnic University of Bucharest. Romania.

Supported by CESDE/USRA.

This work is supported in part by the National Science Foundation under contract number IRI-9357785.

Author currently on leave from the Department of Computer Science, George Mason University. Fairfax, Virginia.

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References

  1. Arunachalan, M., Choudhary, A., Rullman, B., Implementation and evaluation of prefetching in the Intel Paragon Parallel File System. Proceedings of the 10th Int'l. Parallel Processing Symposium, Honolulu. Hawaii, April 15–19, 1996.

    Google Scholar 

  2. Cheung, A. L., Reeves. A. P., Sparse data representation, Proceedings Scalable High Performance Computing Conference. 1992.

    Google Scholar 

  3. Duff, I. S., Sparse matrix test problems, ACM Transactions on Mathematical Software, Vol. 15, No. 1, 1–14, 1989.

    Google Scholar 

  4. Duff, I. S., Grimes, R.G., Lewis, J. G., User's guide for the Harwell-Boeing Sparse Matrix Collection, CERFACS Report TR/PA/92/86.1992.

    Google Scholar 

  5. Nastea, S. G., El-Ghazawi, T., Frieder, O., Parallel input/output impact on sparse matrix compression. Proceedings of the IEEE Data Compression Conference, Snowbird, Utah, 1996.

    Google Scholar 

  6. Nastea, S., El-Ghazawi, T., Frieder, O., A statisticallybased multi-algorithmic approach for load-balancing sparse matrix computations. Proceedings of the IEEE Frontiers of Massively Parallel Computations (Frontiers '96), Annapolis, Maryland, October 1996.

    Google Scholar 

  7. Nastea, S., Frieder, O., El-Ghazawi, T., “Load balancing in sparse matrix-vector multiplication”. Proceedings of the IEEE Symposium of Parallel and Distributed Processing, New Orleans, Louisiana, October 1996.

    Google Scholar 

  8. Paolini, G. V., Santangelo, P., A graphic tool for the structure of large sparse matrices, IBM Technical Report, ICE-0034 IBM ECSEC Rome (1989).

    Google Scholar 

  9. Park, S. C., Draayer, J. P., Zheng, S. Q., Fast sparse matrix multiplication, Computer Physics Communications, Vol. 70, 1992.

    Google Scholar 

  10. Peters, A., Sparse matrix vector multiplication technique on the IBM 3090 VP, Parallel Computing 17, 1991.

    Google Scholar 

  11. Petiton, S., Saad, Y., Wu, K., Ferng, W., Basic Sparse matrix computations on the CM-5, International Journal of Modern Physics C vol. 4, No. 1, 63–83, 1993.

    Google Scholar 

  12. Nitzberg, B., Fineberg, S. A., Parallel I/O on Highly Parallel Systems, Tutorial notes, Proceedings “Supercomputing '94”, Washington D.C., 1994.

    Google Scholar 

  13. Rothberg, E., Schreiber, R., Improved load balancing in parallel sparse Choleski factorization, “Supercomputing '94”, Washington D.C., 1994.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electr. Eng. and Comp. Science, The George Washington University, 20052, Washington, D.C.

    Sorin G. Nastea & Tarek El-Ghazawi

  2. Department of Computer Science, Florida Institute of Technology, 32901-6975, Melbourne, Florida

    Ophir Frieder

Authors
  1. Sorin G. Nastea
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  2. Tarek El-Ghazawi
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  3. Ophir Frieder
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Editor information

Gianfranco Bilardi Afonso Ferreira Reinhard Lüling José Rolim

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© 1997 Springer-Verlag Berlin Heidelberg

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Nastea, S.G., El-Ghazawi, T., Frieder, O. (1997). Performance optimization of combined variable-cost computations and I/O. In: Bilardi, G., Ferreira, A., Lüling, R., Rolim, J. (eds) Solving Irregularly Structured Problems in Parallel. IRREGULAR 1997. Lecture Notes in Computer Science, vol 1253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63138-0_18

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  • DOI: https://doi.org/10.1007/3-540-63138-0_18

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63138-5

  • Online ISBN: 978-3-540-69157-0

  • eBook Packages: Springer Book Archive

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