Sympiler: Transforming sparse matrix codes by decoupling symbolic analysis

Kazem Cheshmi, Shoaib Kamil, Michelle Mills Strout, Maryam Mehri Dehnavi

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

14 Scopus citations

Abstract

Sympiler is a domain-specific code generator that optimizes sparse matrix computations by decoupling the symbolic analysis phase from the numerical manipulation stage in sparse codes. The computation patterns in sparse numerical methods are guided by the input sparsity structure and the sparse algorithm itself. In many real-world simulations, the sparsity pattern changes little or not at all. Sympiler takes advantage of these properties to symbolically analyze sparse codes at compile time and to apply inspector-guided transformations that enable applying low-level transformations to sparse codes. As a result, the Sympiler-generated code outperforms highly-optimized matrix factorization codes from commonly-used specialized libraries, obtaining average speedups over Eigen and CHOLMOD of 3.8x and 1.5x respectively.

Original languageEnglish (US)
Title of host publicationProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450351140
DOIs
StatePublished - Nov 12 2017
EventInternational Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017 - Denver, United States
Duration: Nov 12 2017Nov 17 2017

Publication series

NameProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017

Other

OtherInternational Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017
CountryUnited States
CityDenver
Period11/12/1711/17/17

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications

Keywords

  • Domain-specific compilation
  • Loop transformations
  • Matrix computations
  • Sparse methods

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