Parallel reduction to condensed forms for symmetric eigenvalue problems using aggregated fine-grained and memory-aware kernels

Handle URI:
http://hdl.handle.net/10754/575751
Title:
Parallel reduction to condensed forms for symmetric eigenvalue problems using aggregated fine-grained and memory-aware kernels
Authors:
Haidar, Azzam; Ltaief, Hatem ( 0000-0002-6897-1095 ) ; Dongarra, Jack
Abstract:
This paper introduces a novel implementation in reducing a symmetric dense matrix to tridiagonal form, which is the preprocessing step toward solving symmetric eigenvalue problems. Based on tile algorithms, the reduction follows a two-stage approach, where the tile matrix is first reduced to symmetric band form prior to the final condensed structure. The challenging trade-off between algorithmic performance and task granularity has been tackled through a grouping technique, which consists of aggregating fine-grained and memory-aware computational tasks during both stages, while sustaining the application's overall high performance. A dynamic runtime environment system then schedules the different tasks in an out-of-order fashion. The performance for the tridiagonal reduction reported in this paper is unprecedented. Our implementation results in up to 50-fold and 12-fold improvement (130 Gflop/s) compared to the equivalent routines from LAPACK V3.2 and Intel MKL V10.3, respectively, on an eight socket hexa-core AMD Opteron multicore shared-memory system with a matrix size of 24000×24000. Copyright 2011 ACM.
KAUST Department:
KAUST Supercomputing Laboratory (KSL); Extreme Computing Research Center
Publisher:
Association for Computing Machinery (ACM)
Journal:
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis on - SC '11
Conference/Event name:
2011 International Conference for High Performance Computing, Networking, Storage and Analysis, SC11
Issue Date:
2011
DOI:
10.1145/2063384.2063394
Type:
Conference Paper
ISBN:
9781450307710
Appears in Collections:
Conference Papers; KAUST Supercomputing Laboratory (KSL); Extreme Computing Research Center; Extreme Computing Research Center

Full metadata record

DC FieldValue Language
dc.contributor.authorHaidar, Azzamen
dc.contributor.authorLtaief, Hatemen
dc.contributor.authorDongarra, Jacken
dc.date.accessioned2015-08-24T09:25:14Zen
dc.date.available2015-08-24T09:25:14Zen
dc.date.issued2011en
dc.identifier.isbn9781450307710en
dc.identifier.doi10.1145/2063384.2063394en
dc.identifier.urihttp://hdl.handle.net/10754/575751en
dc.description.abstractThis paper introduces a novel implementation in reducing a symmetric dense matrix to tridiagonal form, which is the preprocessing step toward solving symmetric eigenvalue problems. Based on tile algorithms, the reduction follows a two-stage approach, where the tile matrix is first reduced to symmetric band form prior to the final condensed structure. The challenging trade-off between algorithmic performance and task granularity has been tackled through a grouping technique, which consists of aggregating fine-grained and memory-aware computational tasks during both stages, while sustaining the application's overall high performance. A dynamic runtime environment system then schedules the different tasks in an out-of-order fashion. The performance for the tridiagonal reduction reported in this paper is unprecedented. Our implementation results in up to 50-fold and 12-fold improvement (130 Gflop/s) compared to the equivalent routines from LAPACK V3.2 and Intel MKL V10.3, respectively, on an eight socket hexa-core AMD Opteron multicore shared-memory system with a matrix size of 24000×24000. Copyright 2011 ACM.en
dc.publisherAssociation for Computing Machinery (ACM)en
dc.titleParallel reduction to condensed forms for symmetric eigenvalue problems using aggregated fine-grained and memory-aware kernelsen
dc.typeConference Paperen
dc.contributor.departmentKAUST Supercomputing Laboratory (KSL)en
dc.contributor.departmentExtreme Computing Research Centeren
dc.identifier.journalProceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis on - SC '11en
dc.conference.date12 November 2011 through 18 November 2011en
dc.conference.name2011 International Conference for High Performance Computing, Networking, Storage and Analysis, SC11en
dc.conference.locationSeattle, WAen
dc.contributor.institutionUniversity of Tennessee, 1122 Volunteer Blvd, Knoxville, TN, United Statesen
dc.contributor.institutionOak Ridge National Laboratory, Computer Science and Mathematics Division, United Statesen
dc.contributor.institutionSchool of Computer Science, University of Manchester, United Kingdomen
kaust.authorLtaief, Hatemen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.