Utilizing the power of high-performance computing

Abstract

The main focus of this article is the design of embedded signal processing (ESP) application software. We identify the characteristics of such applications in terms of their computational requirements, data layouts, and latency and throughput constraints. We describe an ESP application, an adaptive sonar beamformer. Then, we briefly survey the state-of-the-art in high performance computing (HPC) technology and address the advantages and challenges in using HPC technology for implementing ESP applications. To describe the software design issues in this context, we define a task model to capture the features of ESP applications. This model specifies the independent activities in each processing stage. We also identify various optimization problems in parallelizing ESP applications. We address the key issues in developing scalable and portable algorithms for ESP applications. We focus on the algorithmic issues in exploiting coarse-grain parallelism. These issues include data layout design and task mapping. We show a task mapping methodology for application software development based on our execution model (Lee et al., 1998). This uses a novel stage partitioning technique to exploit the independent activities in a processing stage. We use our methodology to maximize the throughput of an ESP application for a given platform size. The resulting application software using this methodology is called a software task pipeline. An adaptive sonar beamformer has been implemented using this design methodology.