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The Data Mover: A Machine-Independent Abstraction for Managing Customized Data Motion

  • Scott B. Baden
  • Stephen J. Fink
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1863)

Abstract

This paper discusses the Data Mover, an abstraction for expressing machine-independent customized communication algorithms arising in block-structured computations. The Data Mover achieves performance that is competitive with hand-coding in MPI, but enables application-specific optimization to be expressed using intuitive geometric set operations that encapsulate low-level details.

Keywords

Data Mover Message Passing Interface Member Function Multidimensional Array Message Passing Interface Implementation 
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 2000

Authors and Affiliations

  • Scott B. Baden
    • 1
  • Stephen J. Fink
    • 2
  1. 1.Department of Computer Science and EngineeringUniversity of CaliforniaSan DiegoLa JollaUSA
  2. 2.IBM Thomas J. Watson Research CenterYorktown HeightsUSA

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