A fast and massively-parallel inverse solver for multiple-scattering tomographic image reconstruction

Mert Hidayetoglu, Carl Pearson, Izzat El Hajj, Levent Gurel, Weng Cho Chew, Wen Mei Hwu

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

Abstract

We present a massively-parallel solver for large Helmholtz-Type inverse scattering problems. The solver employs the distorted Born iterative method for capturing the multiples-cattering phenomena in image reconstructions. This method requires many full-wave forward-scattering solutions in each iteration, constituting the main performance bottleneck with its high computational complexity. As a remedy, we use the multilevel fast multipole algorithm (MLFMA). The solver scales among computing nodes using a two-dimensional parallelization strategy that distributes illuminations in one dimension, and MLFMA sub-Trees in the other dimension. Multi-core CPUs and GPUs are used to provide per-node speedup. We demonstrate a 76% efficiency when scaling from 64 GPUs to 4,096 GPUs. The paper provides reconstruction of a 204.8?×204.8? image (4M unknowns) executed on 4,096 GPUs in near-real time (almost 2 minutes). To the best of our knowledge, this is the largest full-wave inverse scattering solution to date, in terms of both image size and computational resources.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages64-74
Number of pages11
ISBN (Print)9781538643686
DOIs
StatePublished - Aug 3 2018
Event32nd IEEE International Parallel and Distributed Processing Symposium, IPDPS 2018 - Vancouver, Canada
Duration: May 21 2018May 25 2018

Publication series

NameProceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018

Other

Other32nd IEEE International Parallel and Distributed Processing Symposium, IPDPS 2018
CountryCanada
CityVancouver
Period5/21/185/25/18

Keywords

  • GPU
  • Imaging
  • Inverse Scattering
  • Massive Parallelization
  • Multilevel Fast Multipole Algorithm

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management

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    Hidayetoglu, M., Pearson, C., El Hajj, I., Gurel, L., Chew, W. C., & Hwu, W. M. (2018). A fast and massively-parallel inverse solver for multiple-scattering tomographic image reconstruction. In Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018 (pp. 64-74). [8425161] (Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IPDPS.2018.00017