I/O conscious algorithm design and systems support for data analysis on emerging architectures

  • Authors:

  • G. Buehrer;A. Ghoting;Xi Zhang;S. Tatikonda;S. Parthasarathy;T. Kurc;J. Saltz

  • Affiliations:

  • The Ohio State University, Columbus, OH;The Ohio State University, Columbus, OH;The Ohio State University, Columbus, OH;The Ohio State University, Columbus, OH;The Ohio State University, Columbus, OH;The Ohio State University, Columbus, OH;The Ohio State University, Columbus, OH

  • Venue:
  • IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
  • Year:
  • 2006

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Abstract

Advances in data collection and storage technologies have given rise to large dynamic data stores. In order to effectively manage and mine such stores on modern and emerging architectures, one must consider both designing effective middleware support and re-architecting algorithms, to derive performance that commensurates with technological advances. In this article, we present a topdown view of how one can achieve this goal for next generation data analysis centers. Specifically, we present a case study on frequent pattern algorithms, and show how such algorithms can be re-structured to be cache, memory and I/O conscious. Furthermore, motivated by such algorithms, we present a services oriented middleware framework for the derivation of high performance on next generation architectures.