Predicting application run times with historical information

We present a technique for predicting the run times of parallel applications based upon the run times of "similar" applications that have executed in the past. The novel aspect of our work is the use of search techniques to determine those application characteristics that yield the best definition of similarity for the purpose of making predictions. We use four workloads recorded from parallel computers at Argonne National Laboratory, the Cornell Theory Center, and the San Diego Supercomputer Center to evaluate the effectiveness of our approach. We show that on these workloads our techniques achieve predictions that are between 21 and 64 percent better than those achieved by other techniques: our approach achieves mean prediction errors that are between 29 and 59 percent of mean application run times. (C) 2004 Published by Elsevier Inc.