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Open AccessArticle

Heterogeneous Compute Clusters and Massive Environmental Simulations Based on the EPIC Model

1
Ecosystems Services and Management Program (ESM), International Institute for Applied Systems Analysis (IIASA), A-2361 Laxenburg, Austria
2
Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, 119991 Moscow, Russia
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Department of Soil Science, Faculty of Natural Sciences, Comenius University in Bratislava, 842 15 Bratislava, Slovakia
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National Agricultural and Food Centre, Soil Science and Conservation Research Institute, 824 80 Bratislava, Slovakia
5
European Commission, Joint Research Centre, 21027 Ispra, Italy
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Environmental Change Institute, Oxford University Centre for the Environment, Oxford OX1 3QY, UK
*
Author to whom correspondence should be addressed.
Modelling 2020, 1(2), 215-224; https://doi.org/10.3390/modelling1020013
Received: 4 November 2020 / Revised: 25 November 2020 / Accepted: 30 November 2020 / Published: 4 December 2020
(This article belongs to the Special Issue Feature Papers of Modelling)
In recent years, the crop growth modeling community invested immense effort into high resolution global simulations estimating inter alia the impacts of projected climate change. The demand for computing resources in this context is high and expressed in processor core-years per one global simulation, implying several crops, management systems, and a several decades time span for a single climatic scenario. The anticipated need to model a richer set of alternative management options and crop varieties would increase the processing capacity requirements even more, raising the looming issue of computational efficiency. While several publications report on the successful application of the original field-scale crop growth model EPIC (Environmental Policy Integrated Climate) for running on modern supercomputers, the related performance improvement issues and, especially, associated trade-offs have only received, so far, limited coverage. This paper provides a comprehensive view on the principles of the EPIC setup for parallel computations and, for the first time, on those specific to heterogeneous compute clusters that are comprised of desktop computers utilizing their idle time to carry out massive computations. The suggested modification of the core EPIC model allows for a dramatic performance increase (order of magnitude) on a compute cluster that is powered by the open-source high-throughput computing software framework HTCondor. View Full-Text
Keywords: EPIC model; high performance computing (HPC); high-throughput computing (HTC); HTCondor; massive spatio-temporal modeling; legacy source code; environmental simulation; heterogeneous compute clusters; crop model; agriculture; climate change EPIC model; high performance computing (HPC); high-throughput computing (HTC); HTCondor; massive spatio-temporal modeling; legacy source code; environmental simulation; heterogeneous compute clusters; crop model; agriculture; climate change
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MDPI and ACS Style

Khabarov, N.; Smirnov, A.; Balkovič, J.; Skalský, R.; Folberth, C.; Van Der Velde, M.; Obersteiner, M. Heterogeneous Compute Clusters and Massive Environmental Simulations Based on the EPIC Model. Modelling 2020, 1, 215-224. https://doi.org/10.3390/modelling1020013

AMA Style

Khabarov N, Smirnov A, Balkovič J, Skalský R, Folberth C, Van Der Velde M, Obersteiner M. Heterogeneous Compute Clusters and Massive Environmental Simulations Based on the EPIC Model. Modelling. 2020; 1(2):215-224. https://doi.org/10.3390/modelling1020013

Chicago/Turabian Style

Khabarov, Nikolay; Smirnov, Alexey; Balkovič, Juraj; Skalský, Rastislav; Folberth, Christian; Van Der Velde, Marijn; Obersteiner, Michael. 2020. "Heterogeneous Compute Clusters and Massive Environmental Simulations Based on the EPIC Model" Modelling 1, no. 2: 215-224. https://doi.org/10.3390/modelling1020013

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