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Publication . Article . 2018

A massively parallel and memory-efficient FEM toolbox with a hybrid total FETI solver with accelerator support

Lubomír Říha; Michal Merta; Radim Vavřík; Tomáš Brzobohatý; Alexandros Markopoulos; Ondřej Meca; Ondřej Vysocký; +2 Authors
Open Access
English
Published: 19 Sep 2018 Journal: The International Journal of High Performance Computing Applications, volume 33, issue 4, pages 660-677 (issn: 1094-3420, eissn: 1741-2846, Copyright policy )
Abstract
In this article, we present the ExaScale PaRallel finite element tearing and interconnecting SOlver (ESPRESO) finite element method (FEM) library, which includes an FEM toolbox with interfaces to professional and open-source simulation tools, and a massively parallel hybrid total finite element tearing and interconnecting (HTFETI) solver which can fully utilize the Oak Ridge Leadership Computing Facility Titan supercomputer and achieve superlinear scaling. This article presents several new techniques for finite element tearing and interconnecting (FETI) solvers designed for efficient utilization of supercomputers with a focus on (i) performance—we present a fivefold reduction of solver runtime for the Laplace equation by redesigning the FETI solver and offloading the key workload to the accelerator. We compare Intel Xeon Phi 7120p and Tesla K80 and P100 accelerators to Intel Xeon E5-2680v3 and Xeon Phi 7210 central processing units; and (ii) memory efficiency—we present two techniques which increase the efficiency of the HTFETI solver 1.8 times and push the limits of the largest possible problem ESPRESO that can solve from 124 to 223 billion unknowns for problems with unstructured meshes. Finally, we show that by dynamically tuning hardware parameters, we can reduce energy consumption by up to 33%.
Subjects by Vocabulary

Microsoft Academic Graph classification: Massively parallel Computer science Xeon Phi Finite element method Reduction (complexity) Xeon FETI Polygon mesh Parallel computing Solver

Subjects

Hardware and Architecture, Theoretical Computer Science, Software

Related Organizations
Funded by
EC| READEX
Project
READEX
Runtime Exploitation of Application Dynamism for Energy-efficient eXascale computing
  • Funder: European Commission (EC)
  • Project Code: 671657
  • Funding stream: H2020 | RIA
Validated by funder
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