research-article

High Performance Computing Enabled Simulation of the Food-Water-Energy System: Simulation of Intensively Managed Landscapes

Authors:

Harold E.B. Dennis,

Robert Henschel,

Holger BrunstAuthors Info & Claims

PEARC '17: Practice and Experience in Advanced Research Computing 2017: Sustainability, Success and Impact

Article No.: 43, Pages 1 - 10

https://doi.org/10.1145/3093338.3093381

Published: 09 July 2017 Publication History

PEARC '17: Practice and Experience in Advanced Research Computing 2017: Sustainability, Success and Impact

High Performance Computing Enabled Simulation of the Food-Water-Energy System: Simulation of Intensively Managed Landscapes

Pages 1 - 10

Abstract
References

Abstract

Domain science experts are commonly limited by computational efficiency of their code and hardware resources available for execution of desired simulations. Here, we detail a collaboration between domain scientists focused on simulating an ensemble of climate and human management decisions to drive environmental (e.g., water quality) and economic (e.g., crop yield) outcomes. Briefly, the domain scientists developed a message passing interface to execute the formerly serial code across a number of processors, anticipating significant performance improvement by moving to a cluster computing environment from their desktop machines. The code is both too complex to efficiently re-code from scratch and has a shared codebase that must continue to function on desktop machines as well as the parallel implementation. However, inefficiencies in the code caused the LUSTRE filesystem to bottleneck performance for all users. The domain scientists collaborated with Indiana University's Science Applications and Performance Tuning and High Performance File System teams to address the unforeseen performance limitations. The non-linear process of testing software advances and hardware performance is a model of the failures and successes that can be anticipated in similar applications. Ultimately, through a series of iterative software and hardware advances the team worked collaboratively to increase performance of the code, cluster, and file system to enable more than 100-fold increases in performance. As a result, the domain science is able to assess ensembles of climate and human forcing on the model, and sensitivities of ecologically and economically important outcomes of intensively managed agricultural landscapes.

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Cited By

Jinxun LXuehe LQiuan ZWenping YQuanzhi YZhen ZQingxi GCarol D(2022)Terrestrial Ecosystem Modeling with IBIS: Progress and Future VisionJournal of Resources and Ecology10.5814/j.issn.1674-764x.2022.01.00113:1Online publication date: 1-Jan-2022
https://doi.org/10.5814/j.issn.1674-764x.2022.01.001
Balson TWard A(2022) A machine learning approach to water quality forecasts and sensor network expansion: Case study in the Wabash River Basin, United States Hydrological Processes10.1002/hyp.1461936:6Online publication date: 14-Jun-2022
https://doi.org/10.1002/hyp.14619
Molins-Ruano PGonzalez-Sacristan CGarcia-Saura C(2018)Phogo: A low cost, free and “maker” revisit to LogoComputers in Human Behavior10.1016/j.chb.2017.09.02980:C(428-440)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1016/j.chb.2017.09.029

Index Terms

High Performance Computing Enabled Simulation of the Food-Water-Energy System: Simulation of Intensively Managed Landscapes
1. Applied computing
  1. Physical sciences and engineering
    1. Earth and atmospheric sciences

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cover image ACM Other conferences

PEARC '17: Practice and Experience in Advanced Research Computing 2017: Sustainability, Success and Impact

July 2017

451 pages

ISBN:9781450352727

DOI:10.1145/3093338

General Chair:
David Hart
National Center for Atmospheric Research

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 09 July 2017

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PEARC17: Practice and Experience in Advanced Research Computing 2017

July 9 - 13, 2017

LA, New Orleans, USA

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PEARC '17 Paper Acceptance Rate 54 of 79 submissions, 68%;

Overall Acceptance Rate 133 of 202 submissions, 66%

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Cited By

Jinxun LXuehe LQiuan ZWenping YQuanzhi YZhen ZQingxi GCarol D(2022)Terrestrial Ecosystem Modeling with IBIS: Progress and Future VisionJournal of Resources and Ecology10.5814/j.issn.1674-764x.2022.01.00113:1Online publication date: 1-Jan-2022
https://doi.org/10.5814/j.issn.1674-764x.2022.01.001
Balson TWard A(2022) A machine learning approach to water quality forecasts and sensor network expansion: Case study in the Wabash River Basin, United States Hydrological Processes10.1002/hyp.1461936:6Online publication date: 14-Jun-2022
https://doi.org/10.1002/hyp.14619
Molins-Ruano PGonzalez-Sacristan CGarcia-Saura C(2018)Phogo: A low cost, free and “maker” revisit to LogoComputers in Human Behavior10.1016/j.chb.2017.09.02980:C(428-440)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1016/j.chb.2017.09.029

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