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Efficient Implementation of Allreduce on BlueGene/L Collective Network

  • George Almási
  • Gábor Dózsa
  • C. Chris Erway
  • Burkhardt Steinmacher-Burow
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3666)

Abstract

BlueGene/L is currently in the pole position on the Top500 list[4]. In its full configuration the system will leverage 65,536 compute nodes. Application scalability is a crucial issue for a system of such size. On BlueGene/L scalability is made possible through the efficient exploitation of special communication. The BlueGene/L system software provides its own optimized version for collective communication routines in addition to the general purpose MPICH2 implementation. The collective network is a natural platform for reduction operations due to its built-in arithmetic units. Unfortunately ALUs of the collective network can handle only fixed point operands. Therefore efficient exploitation of that network for the purpose of floating point reductions is a challenging task. In this paper we present our experiences with implementing an efficient collective network algorithm for Allreduce sums of floating point numbers.

Keywords

Reduction Operation Virtual Channel Interprocessor Communication Collective Network Scratchpad Memory 
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 2005

Authors and Affiliations

  • George Almási
    • 1
  • Gábor Dózsa
    • 1
  • C. Chris Erway
    • 2
  • Burkhardt Steinmacher-Burow
    • 3
  1. 1.IBM T. J. Watson Research CenterYorktown HeightsUSA
  2. 2.Dept. of Comp. SciBrown University ProvidenceUSA
  3. 3.IBM GermanyBoeblingenGermany

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