Algorithm 953

Author:

Granat Robert1,Kågström Bo1,Kressner Daniel2,Shao Meiyue3ORCID

Affiliation:

1. Umeå University, Umeå, Sweden

2. EPF Lausanne, Lausanne, Switzerland

3. Umeå University and EPF Lausanne, Lausanne, Switzerland

Abstract

Library software implementing a parallel small-bulge multishift QR algorithm with Aggressive Early Deflation (AED) targeting distributed memory high-performance computing systems is presented. Starting from recent developments of the parallel multishift QR algorithm [Granat et al., SIAM J. Sci. Comput. 32(4), 2010], we describe a number of algorithmic and implementation improvements. These include communication avoiding algorithms via data redistribution and a refined strategy for balancing between multishift QR sweeps and AED. Guidelines concerning several important tunable algorithmic parameters are provided. As a result of these improvements, a computational bottleneck within AED has been removed in the parallel multishift QR algorithm. A performance model is established to explain the scalability behavior of the new parallel multishift QR algorithm. Numerous computational experiments confirm that our new implementation significantly outperforms previous parallel implementations of the QR algorithm.

Funder

eSSENCE

UMIT Research Lab via an EU Mål 2 project

Swedish Research Council

a strategic collaborative e-Science programme

Publisher

Association for Computing Machinery (ACM)

Subject

Applied Mathematics,Software

Reference39 articles.

1. Numerical linear algebra on emerging architectures: The PLASMA and MAGMA projects

2. M. Ahues and F. Tisseur. 1997. A New Deflation Criterion for the QR Algorithm. LAPACK Working Note 122. M. Ahues and F. Tisseur. 1997. A New Deflation Criterion for the QR Algorithm. LAPACK Working Note 122.

3. Parallel solution of partial symmetric eigenvalue problems from electronic structure calculations

4. ON A BLOCK IMPLEMENTATION OF HESSENBERG MULTISHIFT QR ITERATION

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2. Algorithm 1019: A Task-based Multi-shift QR/QZ Algorithm with Aggressive Early Deflation;ACM Transactions on Mathematical Software;2022-03-31

3. Task‐based, GPU‐accelerated and robust library for solving dense nonsymmetric eigenvalue problems;Concurrency and Computation: Practice and Experience;2020-08-06

4. Introduction to StarNEig—A Task-Based Library for Solving Nonsymmetric Eigenvalue Problems;Parallel Processing and Applied Mathematics;2020

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