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Oblivious vs. Distribution-Based Sorting: An Experimental Evaluation

  • Geeta Chaudhry
  • Thomas H. Cormen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3669)

Abstract

We compare two algorithms for sorting out-of-core data on a distributed-memory cluster. One algorithm, Csort, is a 3-pass oblivious algorithm. The other, Dsort, makes two passes over the data and is based on the paradigm of distribution-based algorithms. In the context of out-of-core sorting, this study is the first comparison between the paradigms of distribution-based and oblivious algorithms. Dsort avoids two of the four steps of a typical distribution-based algorithm by making simplifying assumptions about the distribution of the input keys. Csort makes no assumptions about the keys. Despite the simplifying assumptions, the I/O and communication patterns of Dsort depend heavily on the exact sequence of input keys. Csort, on the other hand, takes advantage of predetermined I/O and communication patterns, governed entirely by the input size, in order to overlap computation, communication, and I/O . Experimental evidence shows that, even on inputs that followed Dsort’s simplifying assumptions, Csort fared well. The running time of Dsort showed great variation across five input cases, whereas Csort sorted all of them in approximately the same amount of time. In fact, Dsort ran significantly faster than Csort in just one out of the five input cases: the one that was the most unrealistically skewed in favor of Dsort. A more robust implementation of Dsort—one without the simplifying assumptions—would run even slower.

Keywords

Communication Pattern Input Size Input Distribution Input Case Beowulf Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Geeta Chaudhry
    • 1
  • Thomas H. Cormen
    • 1
  1. 1.Department of Computer ScienceDartmouth College 

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