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

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Languages and Compilers for Parallel Computing (LCPC 1999)

Part of the book series: Lecture Notes in Computer Science ((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.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of CaliforniaSan Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0114, USA

    Scott B. Baden

  2. IBM Thomas J. Watson Research Center, P.O. Box 704, Yorktown Heights, NY, 10598, USA

    Stephen J. Fink

Authors
  1. Scott B. Baden
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  2. Stephen J. Fink
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0114, USA

    Larry Carter  & Jeanne Ferrante  & 

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Baden, S.B., Fink, S.J. (2000). The Data Mover: A Machine-Independent Abstraction for Managing Customized Data Motion. In: Carter, L., Ferrante, J. (eds) Languages and Compilers for Parallel Computing. LCPC 1999. Lecture Notes in Computer Science, vol 1863. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44905-1_21

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  • DOI: https://doi.org/10.1007/3-540-44905-1_21

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67858-8

  • Online ISBN: 978-3-540-44905-8

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