TY - JOUR

T1 - Parallel implementation of an efficient preconditioned linear solver for grid-based applications in chemical physics. III

T2 - Improved parallel scalability for sparse matrixvector products

AU - Chen, Wenwu

AU - Poirier, Bill

N1 - Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.

PY - 2010/7

Y1 - 2010/7

N2 - The linear solve problems arising in chemical physics and many other fields involve large sparse matrices with a certain block structure, for which special block Jacobi preconditioners are found to be very efficient. In two previous papers [W. Chen, B. Poirier, Parallel implementation of efficient preconditioned linear solver for grid-based applications in chemical physics. I. Block Jacobi diagonalization, J. Comput. Phys. 219 (1) (2006) 185197; W. Chen, B. Poirier, Parallel implementation of efficient preconditioned linear solver for grid-based applications in chemical physics. II. QMR linear solver, J. Comput. Phys. 219 (1) (2006) 198209], a parallel implementation was presented. Excellent parallel scalability was observed for preconditioner construction, but not for the matrixvector product itself. In this paper, we introduce a new algorithm with (1) greatly improved parallel scalability and (2) generalization for arbitrary number of nodes and data sizes.

AB - The linear solve problems arising in chemical physics and many other fields involve large sparse matrices with a certain block structure, for which special block Jacobi preconditioners are found to be very efficient. In two previous papers [W. Chen, B. Poirier, Parallel implementation of efficient preconditioned linear solver for grid-based applications in chemical physics. I. Block Jacobi diagonalization, J. Comput. Phys. 219 (1) (2006) 185197; W. Chen, B. Poirier, Parallel implementation of efficient preconditioned linear solver for grid-based applications in chemical physics. II. QMR linear solver, J. Comput. Phys. 219 (1) (2006) 198209], a parallel implementation was presented. Excellent parallel scalability was observed for preconditioner construction, but not for the matrixvector product itself. In this paper, we introduce a new algorithm with (1) greatly improved parallel scalability and (2) generalization for arbitrary number of nodes and data sizes.

KW - Block Jacobi

KW - Chemical physics

KW - Eigensolver

KW - Linear solver

KW - Matrixvector product

KW - Parallel computing

KW - Preconditioning

KW - Sparse matrix

UR - http://www.scopus.com/inward/record.url?scp=77955412955&partnerID=8YFLogxK

U2 - 10.1016/j.jpdc.2010.03.008

DO - 10.1016/j.jpdc.2010.03.008

M3 - Article

AN - SCOPUS:77955412955

VL - 70

SP - 779

EP - 782

JO - Journal of Parallel and Distributed Computing

JF - Journal of Parallel and Distributed Computing

SN - 0743-7315

IS - 7

ER -