Performance prediction of parallel applications using artificial neuronal network and graph representation

Abstract

SummaryModeling and predicting the performance of HPC applications is exceedingly important for several purposes. These incorporate scheduling and managing tasks, understanding application behavior, and estimating resource requirements. Many issues impact the performance of parallel applications, including data decomposition, load balancing, and inter processors communication. Predicting the performance of parallelism accurately is a challenging task, especially since it depends on several hardware and software parameters that are difficult to identify. The graph representation is an efficient way to model a parallel application. Based on this representation, this study purports to predict the optimal number of partitions (processors) to run efficiently a parallel application on multi‐core architectures using an artificial neural network. The numerical simulation applications are modeled as a graph of tasks and their associated data. Using the characteristics of the graphs as inputs of the ANN model, we obtained satisfactory results with an accuracy of 91% for the optimal number of processors and 93% for the maximum speedup.