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Automatic Performance Optimization of the Discrete Fourier Transform on Distributed Memory Computers

  • Andreas Bonelli
  • Franz Franchetti
  • Juergen Lorenz
  • Markus Püschel
  • Christoph W. Ueberhuber
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4330)

Abstract

This paper introduces a formal framework for automatically generating performance optimized implementations of the discrete Fourier transform (DFT) for distributed memory computers. The framework is implemented as part of the program generation and optimization system Spiral. DFT algorithms are represented as mathematical formulas in Spiral’s internal language SPL. Using a tagging mechanism and formula rewriting, we extend Spiral to automatically generate parallelized formulas. Using the same mechanism, we enable the generation of rescaling DFT algorithms, which redistribute the data in intermediate steps to fewer processors to reduce communication overhead. It is a novel feature of these methods that the redistribution steps are merged with the communication steps of the algorithm to avoid additional communication overhead. Among the possible alternative algorithms, Spiral’s search mechanism now determines the fastest for a given platform, effectively generating adapted code without human intervention. Experiments with DFT MPI programs generated by Spiral show performance gains of up to 30% due to rescaling. Further, our generated programs compare favorably with Fftw-MPI 2.1.5.

Keywords

Discrete Fourier Transform Communication Step Data Redistribution Distribute Memory Computer Discrete Fourier Transform Algorithm 
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 2006

Authors and Affiliations

  • Andreas Bonelli
    • 1
  • Franz Franchetti
    • 2
  • Juergen Lorenz
    • 1
  • Markus Püschel
    • 2
  • Christoph W. Ueberhuber
    • 1
  1. 1.Institute for Analysis and Scientific ComputingVienna University of TechnologyWienAustria
  2. 2.Department of Electrical and Computer EngineeringCarnegie Mellon UniversityPittsburghUSA

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