Fast Approximate Evaluation of Parallel Overhead from a Minimal Set of Measured Execution Times

Abstract

Porting scientific key algorithms to HPC architectures requires a thorough understanding of the subtle balance between gain in performance and introduced overhead. Here we continue the development of our recently proposed technique that uses plain execution times to predict the extent of parallel overhead. The focus here is on an analytic solution that takes into account as many data points as there are unknowns, i.e. model parameters. A test set of 9 applications frequently used in scientific computing can be well described by the suggested model even including atypical cases that were originally not considered part of the development. However, the choice about which particular set of explicit data points will lead to an optimal prediction cannot be made a-priori.

An Investigation of Computation Time Based on Domain Size in WRF Model

“…Basically, the more cores used will speed up the computation process, but at some point, the computation will experience overhead, that is the ineffective runtime due to the need for communication between cores and redundant computing [11], [12]. This is caused by using too many cores.…”