High performance computers are becoming mainstream due to the progress made in hardware as well as software support in the last few years. They can satisfy the STAP computational requirements of real-time applications while increasing the exibility, a ordability, and scalability of radar signal processing systems. However, efficient parallelization of STAP, which consists of several different algorithms is challenging, and requires several optimizations. The design and implementation of the application is portable. In that real-time demonstration, live data from a phased array radar was processed by Intel Paragon machine and results showed that high performance computers can deliver a significant performance again.
Arrows connecting task blocks represent data transfer between tasks.
Using this approach, the throughput may be improved, but the latency is limited by what can be achieved using one compute node. For our parallel implementation of this real application we have designed a model of parallel pipeline system where each pipeline is a collection of tasks and each task itself is parallelized. This parallel pipeline model was applied to the STAP algorithm with each step as a task in a pipeline. This permits us to significantly improve latency as well as throughput. In this paper we present results from this implementation.