Enhancing speed and scalability of the ParFlow simulation code

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Title:Main Title: Enhancing speed and scalability of the ParFlow simulation code
Description:Abstract: Regional hydrology studies are often supported by high resolution simulations of subsurface flow that require expensive and extensive computations. Efficient usage of the latest high performance parallel computing systems becomes a necessity.The simulation software ParFlow has been demonstrated to meet this requirement and shown to have excellent solver scalability for up to 16,384 processes. In the present work we show that the code requires further enhancements in order to fully take advantage of current petascale machines. We identify ParFlow’s way of parallelization of the computational mesh as a central bottleneck. We propose to reorganize this subsystem using fast mesh partition algorithms provided by the parallel adaptive mesh refinement library p4est. We realize this in a minimally invasive manner by modifying selected parts of the code to reinterpret the existing mesh data structures. We evaluate the scaling performance of the modified version of ParFlow, demonstrating good weak and strong scaling up to 458k cores of the Juqueen supercomputer, and test an example application at large scale.
Responsible Party
Creators:Carsten Burstedde (Principal Investigator), Jose A. Fonseca (Author), Stefan Kollet (Principal Investigator)
Publisher:Springer
Publication Year:2017
Topic
TR32 Topic:Other
Related Subproject:D8
Subjects:Keywords: Parallel Computing, Numerical Simulation, Groundwater Flow Model
File Details
Filename:pf_paper.pdf
Data Type:Text - Article
File Size:1.1 MB
Date:Issued: 02.02.2018
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:Completed
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Download Permission:Only Project Members
General Access and Use Conditions:According to the TR32DB data policy agreement.
Access Limitations:According to the TR32DB data policy agreement.
Licence:[TR32DB] Data policy agreement
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Publication Status:Published
Review Status:Peer reviewed
Publication Type:Article
Article Type:Journal
Source:Computational Geosciences
Source Website:https://link.springer.com/journal/10596
Issue:1
Volume:22
Number of Pages:15 (347 - 361)
Metadata Details
Metadata Creator:Jose Fonseca
Metadata Created:03.04.2017
Metadata Last Updated:03.04.2017
Subproject:D8
Funding Phase:3
Metadata Language:English
Metadata Version:V50
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