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

Heterogeneous Compute Clusters and Massive Environmental Simulations Based on the EPIC Model
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.
- Joint Research Centre Italy
- Comenius University Slovakia
- International Institute for Applied Systems Analysis Austria
- National Agricultural and Food Centre Slovakia
- Lomonosov Moscow State University Russian Federation
crop model, EPIC model, high-throughput computing (HTC), heterogeneous compute clusters, high performance computing (HPC), massive spatio-temporal modeling, 004, HTCondor, climate change, environmental simulation, legacy source code, agriculture
crop model, EPIC model, high-throughput computing (HTC), heterogeneous compute clusters, high performance computing (HPC), massive spatio-temporal modeling, 004, HTCondor, climate change, environmental simulation, legacy source code, agriculture
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