P. R. Amestoy, I. S. Duff and C. Vomel, “Task Scheduling in an Asynchronous Distributed Memory Multifrontal Solver,” SIAM Journal on Matrix Analysis and Applications, Vol. 26, No. 2, 2005, pp. 544-565.

has been cited by the following article:

• TITLE: Asynchronous Approach to Memory Management in Sparse Multifrontal Methods on Multiprocessors

  AUTHORS: Alexander Kalinkin, Konstantin Arturov

  KEYWORDS: Direct Solver; Distributed Data; OpenMP and MPI

  JOURNAL NAME: Applied Mathematics, Vol.4 No.12A, December 24, 2013

  ABSTRACT: This research covers the Intel? Direct Sparse Solver for Clusters, the software that implements a direct method for solving the Ax = b equation with sparse symmetric matrix A on a cluster. This method, researched by Intel, is based on Cholesky decomposition and could be considered as extension of functionality PARDISO from Intel?MKL. To achieve an efficient work balance on a large number of processes, the so-called “multifrontal” approach to Cholesky decomposition is implemented. This software implements parallelization that is based on nodes of the dependency tree and uses MPI, as well as parallelization inside a node of the tree that uses OpenMP directives. The article provides a high-level description of the algorithm to distribute the work between both computational nodes and cores within a single node, and between different computational nodes. A series of experiments shows that this implementation causes no growth of the computational time and decreases the amount of memory needed for the computations.