A framework for collective personalized communication

Laxmikant V. Kalé, Sameer Kumar, Krishnan Varadarajan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The paper explores collective personalized communication. For example, in all-to-all personalized communication (AAPC), each processor sends a distinct message to every other processor. However, for many applications, the collective communication pattern is many-to-many, where each processor sends a distinct message to a subset of processors. We first present strategies that reduce per-message cost to optimize AAPC. We then present performance results of these strategies in both all-to-all and many-to-many scenarios. These strategies are implemented in a flexible, asynchronous library with a non-blocking interface, and a message-driven runtime system. This allows the collective communication to run concurrently with the application, if desired. As a result the computational overhead of the communication is substantially reduced, at least on machines such as PSC Lemieux, which sport a co-processor capable of remote DMA. We demonstrate the advantages of our framework with performance results on several benchmarks and applications.

Original languageEnglish (US)
Title of host publicationProceedings - International Parallel and Distributed Processing Symposium, IPDPS 2003
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)0769519261, 9780769519265
DOIs
StatePublished - Jan 1 2003
EventInternational Parallel and Distributed Processing Symposium, IPDPS 2003 - Nice, France
Duration: Apr 22 2003Apr 26 2003

Publication series

NameProceedings - International Parallel and Distributed Processing Symposium, IPDPS 2003

Other

OtherInternational Parallel and Distributed Processing Symposium, IPDPS 2003
CountryFrance
CityNice
Period4/22/034/26/03

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science
  • Software

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