A massively parallel and memory-efficient FEM toolbox with a hybrid total FETI solver with accelerator support
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F19%3A10240450" target="_blank" >RIV/61989100:27230/19:10240450 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27240/19:10240450 RIV/61989100:27740/19:10240450
Result on the web
<a href="https://doi.org/10.1177/1094342018798452" target="_blank" >https://doi.org/10.1177/1094342018798452</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1177/1094342018798452" target="_blank" >10.1177/1094342018798452</a>
Alternative languages
Result language
angličtina
Original language name
A massively parallel and memory-efficient FEM toolbox with a hybrid total FETI solver with accelerator support
Original language description
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%. (C) The Author(s) 2018.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of High Performance Computing Applications
ISSN
1094-3420
e-ISSN
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Volume of the periodical
33
Issue of the periodical within the volume
19.9.2018
Country of publishing house
GB - UNITED KINGDOM
Number of pages
18
Pages from-to
660-677
UT code for WoS article
000471881700007
EID of the result in the Scopus database
2-s2.0-85059519221