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Abstract

preCICE is an open-source library, that provides comprehensive functionality to couple independent parallelized solver codes to establish a partitioned multi-physics multi-code simulation environment. For data communication between the respective executables at runtime, it implements a peer-to-peer concept, which renders the computational cost of the coupling per time step negligible compared to the typical run time of the coupled codes. To initialize the peer-to-peer coupling, the mesh partitions of the respective solvers need to be compared to determine the point-to-point communication channels between the processes of both codes. This initialization effort can become a limiting factor, if we either reach memory limits or if we have to re-initialize communication relations in every time step. In this contribution, we remove two remaining bottlenecks: (i) We base the neighborhood search between mesh entities of two solvers on a tree data structure to avoid quadratic complexity, and (ii) we replace the sequential gather-scatter comparison of both mesh partitions by a two-level approach that first compares bounding boxes around mesh partitions in a sequential manner, subsequently establishes pairwise communication between processes of the two solvers, and finally compares mesh partitions between connected processes in parallel. We show, that the two-level initialization method is fives times faster than the old one-level scheme on 24,567 CPU-cores using a mesh with 628,898 vertices. In addition, the two-level scheme is able to handle much larger computational meshes, since the central mesh communication of the one-level scheme is replaced with a fully point-to-point mesh communication scheme.

Details

1009240
Title
Efficient and Scalable Initialization of Partitioned Coupled Simulations with preCICE
Author
Amin Totounferoush 1 ; Simonis, Frédéric 2 ; Uekermann, Benjamin 1   VIAFID ORCID Logo  ; Schulte, Miriam 1 

 Institute for Parallel and Distributed Systems (IPVS), University of Stuttgart, 70569 Stuttgart, Germany; [email protected] (B.U.); [email protected] (M.S.) 
 Scientific Computing in Computer Science, Technical University of Munich (TUM), 85748 Garching, Germany; [email protected] 
Publication title
Algorithms; Basel
Volume
14
Issue
6
First page
166
Publication year
2021
Publication date
2021
Publisher
MDPI AG
Place of publication
Basel
Country of publication
Switzerland
Publication subject
e-ISSN
19994893
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2021-05-27
Milestone dates
2021-04-30 (Received); 2021-05-25 (Accepted)
Publication history
 
 
   First posting date
27 May 2021
ProQuest document ID
2544558326
Document URL
https://www.proquest.com/scholarly-journals/efficient-scalable-initialization-partitioned/docview/2544558326/se-2?accountid=208611
Copyright
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2023-11-18
Database
ProQuest One Academic