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

  • Sorin G. Nastea
  • Tarek El-Ghazawi
  • Ophir Frieder
Systems and Applications
Part of the Lecture Notes in Computer Science book series (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.

Keywords

load balancing parallel I/O data distribution skewness sparse matrix computations 

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Sorin G. Nastea
    • 1
  • Tarek El-Ghazawi
    • 1
  • Ophir Frieder
    • 2
  1. 1.Department of Electr. Eng. and Comp. ScienceThe George Washington UniversityWashington, D.C.
  2. 2.Department of Computer ScienceFlorida Institute of TechnologyMelbourne

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