A rowwise block-striped partitioning strategy (Figure 4) is used to divide the landscape across the 32 processors. With this data partitioning method, the landscape is divided into groups of complete contiguous rows [KGGK94]. Due to the large computational demand of both the hydrology and vegetation components and the irregular shape of the study area, each processor is assigned a group based on the total number of 500m grid cells (Figure 5), as opposed to the total number of rows, in order to achieve a similar initial workload balance. This partitioning method simplifies the deer movement process (to be discussed in Section 3.3.5), since each processor has only two nearest neighbors. Processor PN's nearest neighbors are PN and PN, with the exception of PN, which has no nearest neighbor to the north, and PN, which has no nearest neighbor to the south.
Figure 4: Example rowwise block-striped data partitioning with 16 rows and 4 processors.
Figure 5: Landscape partitioning according to area.