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Knowledge and cache conscious algorithm design and systems support for data mining algorithms

  • Amol Ghoting
  • , Gregory Buehrer
  • , Matthew Goyder
  • , Shirish Tatikonda
  • , Xi Zhang
  • , Srinivasan Parthasarathy
  • , Tahsin Kurc
  • , Joel Saltz
  • Ohio State University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The knowledge discovery process is interactive in nature and therefore minimizing query response time is imperative. The compute and memory intensive nature of data mining algorithms makes this task challenging. We propose to improve the performance of data mining algorithms by re-architecting algorithms and designing effective systems support. From the view point of re-architecting algorithms, knowledge-conscious and cache-conscious design strategies are presented. Knowledge-conscious algorithm designs try and re-use repeated computation between iterations and across executions of a data mining algorithm. Cache-conscious algorithm designs on the other hand reduce execution time by maximizing data locality and reuse. The design of systems support that allows a variety of data mining algorithms to leverage knowledge-caching and cache-conscious placement with minimal implementation efforts is also presented.

Original languageEnglish
Title of host publicationProceedings - 21st International Parallel and Distributed Processing Symposium, IPDPS 2007; Abstracts and CD-ROM
DOIs
StatePublished - 2007
Event21st International Parallel and Distributed Processing Symposium, IPDPS 2007 - Long Beach, CA, United States
Duration: Mar 26 2007Mar 30 2007

Publication series

NameProceedings - 21st International Parallel and Distributed Processing Symposium, IPDPS 2007; Abstracts and CD-ROM

Conference

Conference21st International Parallel and Distributed Processing Symposium, IPDPS 2007
Country/TerritoryUnited States
CityLong Beach, CA
Period03/26/0703/30/07

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