Predicting parallel applications performance using signatures: The workload effect

TLDR: This work presents a novel approach to model the behavior of message passing parallel applications based in the concept of signatures, which is able to build a model that allows us to predict the application execution time in different systems with variable input data size.

Abstract: Being able to accurately estimate how an application will perform in a specific computational system provides many useful benefits and can result in smarter decisions. In this work we present a novel approach to model the behavior of message passing parallel applications. Based in the concept of signatures, which are the most relevant parts of an application (phases), we are able to build a model that allows us to predict the application execution time in different systems with variable input data size. Executing these signatures with different input data sizes defines a program's behavior partial function. Using regression we can generalize this behavior function to predict an application performance in a target system with other input data size within a predefined range. We explain our methodology and in order to validate the proposal present results using a synthetic program and well known applications.