Scalable parallelization strategies to accelerate NuFFT data translation on multicores

Published

Journal Article

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. © 2010 Springer-Verlag.

Full Text

Duke Authors

Cited Authors

  • Zhang, Y; Liu, J; Kultursay, E; Kandemir, M; Pitsianis, N; Sun, X

Published Date

  • November 19, 2010

Published In

Volume / Issue

  • 6272 LNCS / PART 2

Start / End Page

  • 125 - 136

Electronic International Standard Serial Number (EISSN)

  • 1611-3349

International Standard Serial Number (ISSN)

  • 0302-9743

Digital Object Identifier (DOI)

  • 10.1007/978-3-642-15291-7_13

Citation Source

  • Scopus