The effect of graph partitioning techniques on parallel Block FSAI preconditioning: a computational study

Adaptive Block FSAI (ABF) is a novel preconditioner which has proved efficient for the parallel solution of symmetric positive definite (SPD) linear systems and eigenproblems. A possible drawback stems from its reduced strong scalability, as the iteration count to converge for a given problem tends...

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Bibliographic Details
Published in:Numerical algorithms Vol. 68; no. 4; pp. 813 - 836
Main Authors: Janna, Carlo, Castelletto, Nicola, Ferronato, Massimiliano
Format: Journal Article
Language:English
Published: Boston Springer US 01.04.2015
Springer Nature B.V
Subjects:
Algebra
Algorithms
Blocking
Computer Science
Computer simulation
Counting
Graphs
Iterative methods
Linear systems
Numeric Computing
Numerical Analysis
Original Paper
Partitioning
Preconditioning
Robustness
Theory of Computation
Linear systems
Parallel computing
65F10
68W10
Iterative methods
Preconditioning
65F08
65Y05
ISSN:1017-1398, 1572-9265
Online Access:Get full text
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Summary:Adaptive Block FSAI (ABF) is a novel preconditioner which has proved efficient for the parallel solution of symmetric positive definite (SPD) linear systems and eigenproblems. A possible drawback stems from its reduced strong scalability, as the iteration count to converge for a given problem tends to grow with the number of processors used. The preliminary use of graph partitioning techniques can help improve the preconditioner quality and scalability. According to the specific theoretical properties of Block FSAI, different partitionings are selected and tested in a set of matrices arising from SPD engineering applications. The results show that using an appropriate graph partitioning technique with ABF may play an important role to increase the preconditioner efficiency and robustness, allowing for its effective use also in massively parallel simulations.
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ISSN:1017-1398
1572-9265
DOI:10.1007/s11075-014-9873-5