A Performance Evaluation of QR-eigensolver on IBM Roadrunner cluster for Large Sparse Matrices

The paper presents a performance analysis of theQR eigensolver from ScaLAPACK library on the IBMRoadrunner machine. A ScaLAPACK-based testing platformwas developed in order to evaluate the performance of a parallelsolver to compute the eigenvalues and eigenvectors for largescalesparse matrices. Our...

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Bibliographic Details
Main Authors: Ionela RUSU, Stefan Gh. PENTIUC, Elena Gina CRACIUN, Stefania SOIMAN
Format: Article
Language:English
Published: Stefan cel Mare University of Suceava 2013-01-01
Series:Journal of Applied Computer Science & Mathematics
Subjects:
Online Access:http://jacs.usv.ro/getpdf.php?paperid=14_6
Description
Summary:The paper presents a performance analysis of theQR eigensolver from ScaLAPACK library on the IBMRoadrunner machine. A ScaLAPACK-based testing platformwas developed in order to evaluate the performance of a parallelsolver to compute the eigenvalues and eigenvectors for largescalesparse matrices. Our experiments showed encouragingresults on the IBM Roadrunner cluster, the acceleration factorgained was up to 40 for large matrices. This result is bright tosolve problems that involve scientific and large-scale computing.
ISSN:2066-4273
2066-3129