High-speed parallel external sorting of data with arbitrary distribution

Minsoo Jeon, Dongseung Kim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Many parallel sorting algorithms of external disk data have been reported such as NOW-sort, SPsort, hill sort and so on. They all reduce the execution time compared with some known sequential sort; however, they differ in terms of the speed, throughput or cost-effectiveness. Mostly, they deal with uniformly distributed data in their value range. If we divide and redistribute data to processors by fixed and equal division of the key range, all processors will have about equal numbers of keys to sort and store. But if irregularly distributed data are given, the performance will suffer severely as the partitioning would no longer produce balanced loads among processors. Few research results have been reported for parallel external sort of data with arbitrary distribution. In this paper, we develop two distribution-insensitive scalable parallel external sorting algorithms that use sampling technique and histogram counts to achieve even distribution of keys, which eventually contribute to achieve good performance. Experimental results on a cluster of 16 Linux workstations show up to threefold enhancement of the performance compared with NOW-sort for sorting 16 GB integer keys.

Original languageEnglish
Pages (from-to)36-44
Number of pages9
JournalInternational Journal of High Performance Computing and Networking
Issue number1
Publication statusPublished - 2004


  • NOW-sort
  • cluster
  • external sort
  • histogram
  • load balancing
  • sample sort

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications


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