Comparison of distributed parallel scheduling schemes for crop growth model

In order to improve the computing speed of crop growth models, multi-distributed parallel scheduling schemes were proposed. The data dependency relationships and calculation process for sub-model and sub-model’s internal components in field scale were analyzed. Based on the pipeline technology and the separate handling strategy, different distributed parallel scheduling schemes for sub-model components layer, sub-models layer and driver data layer were designed respectively. The parallel simulation scheduling schemes were realized by using programming models of OpenMP, MPI and OpenMP mixed, or MPI in the Windows Compute Cluster Server 2003 (WCCS2003) cluster environment. The results of parallel speedup experiment indicated that the optimized parallel scheme of sub-models layer could achieve average speedup to 8.2 in a PC cluster with six dual-core CPUs, which was close to the predicted value of parallel computing speedup for crop growth model. The medium granularity parallel scheduling schemes in sub-models layer based on MPI has a faster computing speed, and it is more suitable for crop growth simulation system in distributed cluster environment.