Comparative study of one-sided factorizations with multiple software packages on multi-core hardware

Emmanuel Agullo*, Bilel Hadri, Hatem Ltaief, Jack Dongarrra

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

46 Scopus citations

Abstract

The emergence and continuing use of multi-core architectures require changes in the existing software and sometimes even a redesign of the established algorithms in order to take advantage of now prevailing parallelism. The Parallel Linear Algebra for Scalable Multi-core Architectures (PLASMA) is a project that aims to achieve both high performance and portability across a wide range of multi-core architectures. We present in this paper a comparative study of PLASMA's performance against established linear algebra packages (LAPACK and ScaLAPACK), against new approaches at parallel execution (Task Based Linear Algebra Subroutines - TBLAS), and against equivalent commercial software offerings (MKL, ESSL and PESSL). Our experiments were conducted on one-sided linear algebra factorizations (LU, QR and Cholesky) and used multi-core architectures (based on Intel Xeon EMT64 and IBM Power6). A performance improvement of 67% was for instance obtained on the Cholesky factorization of a matrix of order 4000, using 32 cores.

Original languageEnglish (US)
Title of host publicationProceedings of the Conference on High Performance Computing Networking, Storage and Analysis, SC '09
DOIs
StatePublished - Dec 1 2009
EventConference on High Performance Computing Networking, Storage and Analysis, SC '09 - Portland, OR, United States
Duration: Nov 14 2009Nov 20 2009

Publication series

NameProceedings of the Conference on High Performance Computing Networking, Storage and Analysis, SC '09

Other

OtherConference on High Performance Computing Networking, Storage and Analysis, SC '09
CountryUnited States
CityPortland, OR
Period11/14/0911/20/09

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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