For distributed memory architectures, communication cost is a significant source of overhead in parallel scientific applications. Many proposed communication optimizations duplicate the behavior of well-written hand-tuned parallel code. Because of continuous changes in architectural components, these types of low-level optimizations are not always effective. This thesis seeks to develop a high-level optimization of work replication in which computations are replicated to minimize communications. There exist performance trade-offs between computation cost and communication cost because of work replication. Due to these trade-offs, it is required to determine which computations should be replicated to improve overall performance. This research presents the development of a model-based approach with heuristics to automatically determine the computations to replicate. Using a computational and communication model, the execution time is predicted to make replication decisions.