Measuring and Analyzing Parallel Computing Scalability

Abstract

In this paper we compare a group of existing scalability metrics and evaluation models through experiments and analyses. This work is mainly motivated by an experimental metric using network latency to measure and evaluate the scalability of parallel programs and architectures. Experiments of running a large physics simulation program and its scaling variations on the KSR-1 multiprocessor have been intensively conducted in this study. The scalability data are collected and visualized by an integrated software environment, called S-Graph. Our objectives are to address fundamental issues of parallel computing scalability, such as evaluation models, analytical and experimental metrics, and measurement methodology; to examine latency patterns inherent in the architecture and the program and their effects on scalability; and to evaluate relative merits and limits of several existing metrics. Our comparisons indicate that both time-bound and memory-bound models have their limitations for evaluating scalability performance, while efficiency-based model could obtain reasonably precise measurements.