Advanced Support for Multilevel Heterogeneous Embedded High Performance Computing

摘要

Embedded systems often must adhere to strict size, 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 can 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.