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Shared-memory parallelization of MTTKRP for dense tensors

Published:10 February 2018Publication History
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Abstract

The matricized-tensor times Khatri-Rao product (MTTKRP) is the computational bottleneck for algorithms computing CP decompositions of tensors. In this work, we develop shared-memory parallel algorithms for MTTKRP involving dense tensors. The algorithms cast nearly all of the computation as matrix operations in order to use optimized BLAS subroutines, and they avoid reordering tensor entries in memory. We use our parallel implementation to compute a CP decomposition of a neuroimaging data set and achieve a speedup of up to 7.4X over existing parallel software.

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  • Published in

    cover image ACM SIGPLAN Notices
    ACM SIGPLAN Notices  Volume 53, Issue 1
    PPoPP '18
    January 2018
    426 pages
    ISSN:0362-1340
    EISSN:1558-1160
    DOI:10.1145/3200691
    Issue’s Table of Contents
    • cover image ACM Conferences
      PPoPP '18: Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
      February 2018
      442 pages
      ISBN:9781450349826
      DOI:10.1145/3178487

    Copyright © 2018 ACM

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 10 February 2018

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