Design, implementation and evaluation of parallel pipelined STAP onparallel computers

Performance results are presented for the design and implementation of parallel pipelined space-time adaptive processing (STAP) algorithms on parallel computers. In particular, the issues involved in parallelization, our approach to parallelization, and performance results on an Intel Paragon are described. The process of developing software for such an application on parallel computers when latency and throughput are both considered together is discussed and tradeoffs considered with respect to inter and intratask communication and data redistribution are presented. The results show that not only scalable performance was achieved for individual component tasks of STAP but linear speedups were obtained for the integrated task performance, both for latency as well as throughput. Results are presented for up to 236 compute nodes (limited by the machine size available to us). Another interesting observation made from the implementation results is that performance improvement due to the assignment of additional processors to one task can improve the performance of other tasks without any increase in the number of processors assigned to them. Normally, this cannot be predicted by theoretical analysis     

Real-Time STAP demonstration on an embedded high performancecomputer

Real-time signal processing for a 16-channel phased array radar, including space-time adaptive processing (STAP) algorithms, has been implemented using a 29-node ruggedized version of an Intel Paragon. Techniques employed to efficiently implement each step of the signal processing are discussed. An overall throughput of 3.15 GFLOPS and processing efficiency of 48% has been achieved, indicating that embedded high performance computers can deliver a significant percentage of their advertised peak throughput under real system constraints