Food

Agroecosystem models that can incorporate management practices and quantify environmental effects
are necessary to assess sustainability-associated food and bioenergy production across spatial scales.
However, most agroecosystem models are designed for a plot scale. Tremendous computational capacity
on simulations and datasets is needed when large scales of high-resolution spatial simulations are conducted.
We used the message passing interface (MPI) parallel technique and developed a master–slave
scheme for an agroecosystem model, EPIC on global food and bioenergy studies. Simulation performance
was further enhanced by applying the Vampir framework. On a Linux-based supercomputer, Cray XT7
Titan, we used 2048 cores and successfully shortened the running time from days to 30 min for a global
30 years of modeling of a bioenergy crop at the resolution of half-degree (62,482 grids) with the message
passing interface based EPIC (mpi_EPIC). The results illustrate that mpi_EPIC using parallel design can
balance simulation workloads and facilitate large-scale, high-resolution analyses of agricultural production
systems, management alternatives and environmental effects.