Parallel map analysis on the CM-5 for landscape ecology models

In landscape ecology, computer modeling is used to assess habitat fragmentation and its ecological implications. Specifically, maps (2-D grids) of habitat clusters are analyzed to determine numbers, sizes, and geometry of clusters. Previous ecological models have relied upon sequential Fortran-77 programs which have limited the size and density of maps that can be analyzed. To efficiently analyze relatively large maps, we present parallel map analysis software implemented on the CM-5. For algorithm development, random maps of different sizes and densities were generated and analyzed. Initially, the Fortran-77 program was rewritten in C, and the sequential cluster identification algorithm was improved and implemented as a recursive or nonrecursive algorithm. The major focus of parallelization was on cluster geometry using C with CMMD message passing routines. Several different parallel models were implemented: host/node, hostless, and host/node with vector units (VUs). All models obtained some speed improvements when compared against several RISC-based workstations. The host/node model with VUs proved to be the most efficient and flexible with speed improvements for a 512 x 512 map of 187, 95, and 20 over the Sun Sparc 2, HP 9000-750, and IBM RS/6000-350, respectively. When tested on an actual map produced through remote imagery and used in ecological studies this same model obtained a speed improvement of 119 over the Sun Sparc 2.