PFEAST: A High Performance Sparse Eigenvalue Solver Using Distributed-Memory Linear Solvers

James Kestyn, Vasileios Kalantzis, Eric Polizzi, Yousef Saad

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

19 Scopus citations

Abstract

The FEAST algorithm and eigensolver for interior eigenvalue problems naturally possesses three distinct levels of parallelism. The solver is then suited to exploit modern computer architectures containing many interconnected processors. This paper highlights a recent development within the software package that allows the dominant computational task, solving a set of complex linear systems, to be performed with a distributed memory solver. The software, written with a reverse-communication-interface, can now be interfaced with any generic MPI linear-system solver using a customized data distribution for the eigenvector solutions. This work utilizes two common 'black-box' distributed memory linear-systems solvers (Cluster-MKL-Pardiso and MUMPS), as well as our own application-specific domain-decomposition MPI solver, for a collection of 3-dimensional finite-element systems. We discuss and analyze how parallel resources can be placed at all three levels simultaneously in order to achieve good scalability and optimal use of the computing platform.

Original languageEnglish (US)
Title of host publicationProceedings of SC 2016
Subtitle of host publicationThe International Conference for High Performance Computing, Networking, Storage and Analysis
PublisherIEEE Computer Society
Pages178-189
Number of pages12
ISBN (Electronic)9781467388153
DOIs
StatePublished - Jul 2 2016
Event2016 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2016 - Salt Lake City, United States
Duration: Nov 13 2016Nov 18 2016

Publication series

NameInternational Conference for High Performance Computing, Networking, Storage and Analysis, SC
Volume0
ISSN (Print)2167-4329
ISSN (Electronic)2167-4337

Other

Other2016 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2016
Country/TerritoryUnited States
CitySalt Lake City
Period11/13/1611/18/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Fingerprint

Dive into the research topics of 'PFEAST: A High Performance Sparse Eigenvalue Solver Using Distributed-Memory Linear Solvers'. Together they form a unique fingerprint.

Cite this