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Scalable Parallelization Strategies to Accelerate NuFFT Data Translation on Multicores

  • Yuanrui Zhang
  • Jun Liu
  • Emre Kultursay
  • Mahmut Kandemir
  • Nikos Pitsianis
  • Xiaobai Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6272)

Abstract

The non-uniform FFT (NuFFT) has been widely used in many applications. In this paper, we propose two new scalable parallelization strategies to accelerate the data translation step of the NuFFT on multicore machines. Both schemes employ geometric tiling and binning to exploit data locality, and use recursive partitioning and scheduling with dynamic task allocation to achieve load balancing. The experimental results collected from a commercial multicore machine show that, with the help of our parallelization strategies, the data translation step is no longer the bottleneck in the NuFFT computation, even for large data set sizes, with any input sample distribution.

Keywords

Fast Fourier Transform Conventional Tiling Mutual Exclusion Cartesian Grid Cache Size 
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 2010

Authors and Affiliations

  • Yuanrui Zhang
    • 1
  • Jun Liu
    • 1
  • Emre Kultursay
    • 1
  • Mahmut Kandemir
    • 1
  • Nikos Pitsianis
    • 2
    • 3
  • Xiaobai Sun
    • 3
  1. 1.Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Aristotle UniversityThessalonikiGreece
  3. 3.Duke UniversityDurhamU.S.A.

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