A Metric for the Temporal Characterization of Parallel Programs

We consider the time-dependent demands for data movement that a parallel program makes on the architecture that executes it. The result is an architecture-independent metric that represents the temporal behavior of data-movement requirements. Programs are described as series of computations and data movements, and while message passing is not ruled out, we focus on explicit parallel programs using a fixed number of processes in a distributed shared-memory environment. Operations are assumed to be explicitly allocated to processors when the metric is applied, which might correspond to intermediate code in a parallelizing compiler. The metric is called the interprocess read (IR) temporal metric. A key to developing an architecture-independent temporal metric is modeling program execution time in an architecture-independent way. This is possible because well-synchronized parallel programs make coordinated progress above a certain level of granularity. Our execution time characterization takes into account barrier synchronization and critical sections. We illustrate the metric using instruction count on simple code fragments and then from multiprocessor program traces (Splash benchmarks). Results of running the benchmarks on simulated network architectures show that the IR metric for the time scale of network response predicts performance better than whole program measures.