@inproceedings{
 title = {High performance computing enabled simulation of the food-water-energy system},
 type = {inproceedings},
 year = {2017},
 keywords = {Agricultural machinery,Agriculture,Agro ecosystems,Agro-IBIS,Benchmarking,Climate model,Computer clusters,Fi},
 pages = {10},
 volume = {Part F1287},
 websites = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85025829960&doi=10.1145%2F3093338.3093381&partnerID=40&md5=69d8a119998a6e0aac5a0a4ac12415ad},
 publisher = {Association for Computing Machinery},
 id = {daec685b-ff26-3d1e-8dee-69e36ead6ddf},
 created = {2019-10-01T17:20:37.493Z},
 file_attached = {false},
 profile_id = {42d295c0-0737-38d6-8b43-508cab6ea85d},
 last_modified = {2019-10-01T17:26:01.286Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Dennis2017},
 source_type = {conference},
 notes = {cited By 0; Conference of 2017 Practice and Experience in Advanced Research Computing, PEARC 2017 ; Conference Date: 9 July 2017 Through 13 July 2017; Conference Code:128771},
 folder_uuids = {22c3b665-9e84-4884-8172-710aa9082eaf,3b35931e-fb6d-48f9-8e01-87ee16ef0331},
 private_publication = {false},
 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. Brie.y, the domain scientists developed a message passing interface to execute the formerly serial code across a number of processors, anticipating signi.cant 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, ineff-ciencies in the code caused the LUSTRE .lesystem to bo.leneck 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 so.ware 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 so.ware and hardware advances the team worked collaboratively to increase performance of the code, cluster, and .le 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. © 2017 Copyright is held by the owner/author(s).},
 bibtype = {inproceedings},
 author = {Dennis, H E B and Ward, A S and Balson, T and Li, Y and Henschel, R and Slavin, S and Simms, S and Brunst, H},
 doi = {10.1145/3093338.3093381},
 booktitle = {Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact (PEARC17)}
}

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