A Fully Asynchronous Multifrontal Solver Using Distributed Dynamic Scheduling

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Authors Patrick Amestoy, Iain S. Duff, Jean-Yves L'Excellent, Jacko Koster
Journal/Conference Name SIAM J. Matrix Analysis Applications
Paper Category
Paper Abstract In this paper, we analyze the main features and discuss the tuning of the algorithms for the direct solution of sparse linear systems on distributed memory computers developed in the context of a long term European research project. The algorithms use a multifrontal approach and are especially designed to cover a large class of problems. The problems can be symmetric positive definite, general symmetric, or unsymmetric matrices, both possibly rank deficient, and they can be provided by the user in several formats. The algorithms achieve high performance by exploiting parallelism coming from the sparsity in the problem and that available for dense matrices. The algorithms use a dynamic distributed task scheduling technique to accommodate numerical pivoting and to allow the migration of computational tasks to lightly loaded processors. Large computational tasks are divided into subtasks to enhance parallelism. Asynchronous communication is used throughout the solution process to efficiently overlap communication with computation.
Date of publication 1999
Code Programming Language R
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