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Embedded systems often must adhere to strict sire, weight, and power (SWAP) constraints and yet provide tremendous computational throughput. Increasing the difficulty of this challenge, there is a trend to utilize commercial-of-the-shelf (COTS) components in the design of such systems to reduce both total cost and time to market. Two embedded high performance radar applications are investigated in this effort: synthetic aperture radar (SAR) and space-time adaptive processing (STAP). Advanced techniques for optimally configuring and utilizing the components of a commercially particular multicomputer platform are described for these two applications. Although a particular platform is target in this study - Mercury Computer Systems' RACE multicomputer - the techniques described in this report are generic and could be applied to a range of different computational platforms. For the SAR application, a system performance model in the context of SWAP, is developed based on mathematical programming. An optimization technique using a combination of constrained nonlinear and integer programming is developed to determine system configurations that minimize SWAP. A major challenge of implementing parallel STAP algorithms on multiprocessor systems is determining the best method for distributing the 3-D data cube across processors of the multiprocessor system and scheduling communication within each phase of computation.