Impact of data distribution on the parallel performance of iterative linear solvers with emphasis on CFD of incompressible flows

• Published in: Computational mechanics Vol. 55; no. 1; pp. 93 - 103
• Main Authors: Esmaily-Moghadam, M., Bazilevs, Y., Marsden, A. L.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2015; Springer; Springer Nature B.V
• Subjects: Algorithms; Classical and Continuum Physics; Computation; Computational efficiency; Computational fluid dynamics; Computational Science and Engineering; Data structures; Engineering; Fluid flow; Incompressible flow; Iterative methods; Iterative solution; Linear equations; Linear systems; Mathematical analysis; Mathematical models; Matrix algebra; Matrix methods; Microprocessors; Original Paper; Solvers; Theoretical and Applied Mechanics; Linear solver; Parallel data structure; Finite element
• ISSN: 0178-7675; 1432-0924
• DOI: 10.1007/s00466-014-1084-3

A parallel data structure that gives optimized memory layout for problems involving iterative solution of sparse linear systems is developed, and its efficient implementation is presented. The proposed method assigns a processor to a problem subdomain, and sorts data based on the shared entries with the adjacent subdomains. Matrix–vector-product communication overhead is reduced and parallel scalability is improved by overlapping inter-processor communications and local computations. The proposed method simplifies the implementation of parallel iterative linear equation solver algorithms and reduces the computational cost of vector inner products and matrix–vector products. Numerical results demonstrate very good performance of the proposed technique.