Abstract

Parallelism Metrics Previous studies have shown that a few key parameters such as the fraction of sequential work [Amdahl 1967], average parallelism [Eager et al. 1989], and variance of parallelism [Sevcik 1989] can each provide a concise yet powerful characterization of program performance. For example, Eager et al. have derived bounds on speedup for arbitrary, work-conserving, task scheduling functions using the average parallelism (A) alone [Eager et al. 1989]. Sevcik has shown that for scheduling parallel jobs on a multiprocessor system, where only very concise information about the arriving jobs can be used, effective scheduling is possible using just two parameters, average parallelism and variance of parallelism [Sevcik 1989]. While simple parameters like the fraction of sequential work and Pmax can be easily estimated for a given program, other parameters like A and the variance of parallelism are much more difficult to obtain. (For example, consider estimating A for PSIM or ...

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