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Parallel MLEM on multicore architectures

  • Carsten Trinitis
    ,
  • Tilman Kustner
    ,
  • Josef Weidendorfer
    ,
  • Jasmine Schirmer
    ,
  • Tobias Klug
    ,
  • Sybille Ziegler
Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Abstract

The efficient use of multicore architectures for sparse matrix-vector multiplication (SpMV) is currently an open challenge. One algorithm which makes use of SpMV is the maximum likelihood expectation maximization (MLEM) algorithm. When using MLEM for positron emission tomography (PET) image reconstruction, one requires a particularly large matrix. We present a new storage scheme for this type of matrix which cuts the memory requirements by half, compared to the widely-used compressed sparse row format. For parallelization we combine the two partitioning techniques recursive bisection and striping. Our results show good load balancing and cache behavior. We also give speedup measurements on various modern multicore systems.

Publication Information

Output type

Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Original language

English

Publication milestones

  • Published - 20/05/2009

Publication status

Published - 20/05/2009

Publisher

Springer, Japan, India, Australia, Germany, United States, United Arab Emirates, Austria, Switzerland, Italy, China, United Kingdom, Netherlands, Brazil, France, Singapore

ISBN (Print)

9783642019692

Publication IDs

  • handle.net: 10547/272038
  • Scopus: 68849087154

Host publication title

nan

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Related Event

Title

9th International Conference on Computational Science

Description

9th International Conference on Computational Science

Event type

Conference

Date

20/05/2009