Including the Workload Effect in the Parallel Program Signature

Performance prediction and application behavior modeling have been the subject of extensive research that aims to estimate applications performance with acceptable precision. In this paper we present a novel approach to model the behavior of message passing parallel applications. There are many dimensions to consider while predicting a deterministic application behavior. Two dimensions that affect an application performance are the computational resources available and the size of its input data used in the computation. Based on the concept of signatures, we are able to build a model that allows us to predict applications execution time in different systems with variable input data size within a predefined range. Our approach generates signatures, which consist of the most relevant parts of an application (phases). Executing these phases for different workloads partially defines a program's behavior function. By using regression analysis we are able to generalize this behavior function to predict an application performance in a target system with any input data size within a predefined range. We explain our methodology and in order to validate the proposal, we present results using a synthetic program and well-known applications. We were able to estimate the total execution time for a input data size range with an average error of 4 % executing, at most, three signatures that represent less than the 10 % of the total application execution time.