As high end computing systems continue to scale in CPU computational power and overall node count, optimization techniques that can reduce communication overhead have proven important. Communication optimizations have been explored in the context of parallelizing compilers and data parallel languages. Most of these studies have traditionally been performed using MPI as the communication library and at a time when networks had a relatively high latency and low bandwidth. As a result, most techniques concentrate on eliminating redundant messages and reducing message count through aggregation. Research on recent networks has shown that significant performance improvements can be achieved using fine grained communication decomposition and overlap.