The FEAST algorithm and eigensolver for interior eigenvalue problems naturally possesses three distinct levels of parallelism. The solver is then suited to exploit modern computer architectures containing many interconnected processors. This paper highlights a recent development within the software package that allows the dominant computational task, solving a set of complex linear systems, to be performed with a distributed memory solver. The software, written with a reverse-communication-interface, can now be interfaced with any generic MPI linear-system solver using a customized data distribution for the eigenvector solutions. This work utilizes two common 'black-box' distributed memory linear-systems solvers (Cluster-MKL-Pardiso and MUMPS), as well as our own application-specific domain-decomposition MPI solver, for a collection of 3-dimensional finite-element systems. We discuss and analyze how parallel resources can be placed at all three levels simultaneously in order to achieve good scalability and optimal use of the computing platform.
|Original language||English (US)|
|Title of host publication||Proceedings of SC 2016|
|Subtitle of host publication||The International Conference for High Performance Computing, Networking, Storage and Analysis|
|Publisher||IEEE Computer Society|
|Number of pages||12|
|State||Published - Jul 2 2016|
|Event||2016 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2016 - Salt Lake City, United States|
Duration: Nov 13 2016 → Nov 18 2016
|Name||International Conference for High Performance Computing, Networking, Storage and Analysis, SC|
|Other||2016 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2016|
|City||Salt Lake City|
|Period||11/13/16 → 11/18/16|
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© 2016 IEEE.