HPC-EPIC for high resolution simulations of environmental and sustainability assessment

Multiple concerns over the impact of wide scale changes in land management have motivated comprehensive analyses of environmental sustainability of food and biofuel production. These call for high-resolution land management tools that enable comprehensive analyses of natural resources for decision-making. The agroecosystem simulation models with the most biophysical detail are point models, which often have a user interface that allows users to provide inputs and examine results for agricultural field scale analyses. These are not able to meet the needs of high-resolution regional or national simulations. We describe an efficient computational approach for deployment of the Environmental Policy Integrated Climate (EPIC) model at high-resolution spatial scales using high performance computing (HPC) techniques. We developed an integrated procedure for executing the millions of simulations required for high-resolution, regional studies, and also address building databases for model initialization, model forcing data, and model outputs. We first ported EPIC from Windows to an HPC platform and validated output from both platforms. We then developed methods of packaging simulations for efficient, unattended parallel execution on the HPC cluster. The job queuing system, Portable Batch System (PBS) is employed to control job submission. Simulation outputs are extracted to PostgreSQL database for analysis. In a case study covering four counties in central Wisconsin using HPC-EPIC, we finished over 140 K simulations in a total of 10 h on an HPC cluster using 20 nodes. This is a speedup of 40 times. More nodes could be used to achieve larger speedups. The HPC-EPIC model developed in this study is anticipated to provide information useful for high-resolution land use management and decision making. The framework for high-performance computing can be extended to other traditional, point-based biophysical simulation models.

► Framework for regional and national simulation with high spatial resolutions.
► Apply HPC to compute millions of point simulations fully utilizing commodity clusters.
► Template for distributed computation of regional simulations for standalone models.