Extraction of Parallel Application Signatures for Performance Prediction

Predicting performance of parallel applications is becoming increasingly complex and the best performance predictor is the application itself, but the time required to run it thoroughly is a onerous requirement. We seek to characterize the behavior of message-passing applications on different systems by extracting a signature which will allow us to predict what system will allow the application to perform best. To achieve this goal, we have developed a method we called Parallel Application Signatures for Performance Prediction (PAS2P) that strives to describe an application based on its behavior. Based on the application´s message-passing activity, we have been able to identify and extract representative phases, with which we created a Parallel Application Signature that has allowed us to predict the application´s performance. We have experimented with different signature-extraction algorithms and found a reduction in the prediction error using different scientific applications on different clusters. We were able to predict execution times with an average accuracy of over 98%.