Scalable parallel computers for real-time signal processing

Kai Hwang, Zhiwei Xu
1996 IEEE Signal Processing Magazine  
n this article, we assess the state-of-the-art technology in massively parallel processors (MPPs) and their variations in different architectural platforms. Architectural and programming issues are identified in using MPPs for time-critical applications such as adaptive radar signal processing. First, we review the enabling technologies. These include high-performance CPU chips and system interconnects, distributed memory architectures, and various latency hiding mechanisms. We characterize the
more » ... We characterize the concept of scalability in three areas: resources, applications, and technology. Scalable performance attributes are analytically defined. Then we compare MPPs with symmetric multiprocessors (SMPs) and clusters of workstations (COWS). The purpose is to reveal their capabilities, limits, and effectiveness in signal processing. In particular, we evaluate the IBM SP2 at MHPCC [33], the Intel Paragon at SDSC [38], the Cray T3D at Cray Eagan Center [I], and the Cray T3E and ASCI TeraFLOP system recently proposed by Intel [32]. On the software and programming side, we evaluate existing parallel programming environments, including the models, languages, compilers, software tools, and operating systems. Some guidelines for program parallelization are provided. We examine data-parallel, shared-variable, message-passing, and implicit programming models. Communication functions and their performance overhead are discussed. Available software tools andcommunication libraries are introduced. Our experiences in porting the MITLincoln Laboratory STAP (space-time adaptive processing) benchmark programs onto the SP2, T3D, and Paragon are reported. Benchmark performance results are presented along with some scalability analysis on machine and problem sizes. Finally, we comment on using these scalable computers for signal processing in the future.
doi:10.1109/79.526898 fatcat:lqng5sb2rvei5jedkzgkl5jz6y