Large-scale algorithm design for parallel FFT-based simulations on GPUs

Anuva Kulkarni, Franz Franchetti, Jelena Kovacevic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We describe and analyze a co-design of algorithm and software for high-performance simulation of a partial differential equation (PDE) numerical solver for large-scale datasets. Large-scale scientific simulations involving parallel Fast Fourier Transforms (FFTs) have extreme memory requirements and high communication cost. This hampers high resolution analysis with fine grids. Moreover, it is difficult to accelerate legacy Fortran scientific codes with modern hardware such as GPUs because of memory constraints of GPUs. Our proposed solution uses signal processing techniques such as lossy compression and domain-local FFTs to lower iteration cost without adversely impacting accuracy of the result. In this work, we discuss proof-of-concept results for various aspects of algorithm development.

Original languageEnglish (US)
Title of host publication2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages301-305
Number of pages5
ISBN (Electronic)9781728112954
DOIs
StatePublished - Feb 20 2019
Event2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Anaheim, United States
Duration: Nov 26 2018Nov 29 2018

Publication series

Name2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings

Conference

Conference2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
CountryUnited States
CityAnaheim
Period11/26/1811/29/18

Keywords

  • Algorithm design
  • GPU
  • Irregular domain decomposition
  • Lossy compression

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

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