Caracterización de aplicaciones con comportamiento irregular para predecir su rendimiento, basado en la filosofía PAS2P.

Abstract

Modeling parallel scientific applications allows us to understand the intricacies of parallel application behavior. Many scientific applications exhibit irregular behavior, which makes it difficult to obtain the model that characterizes the application. This difficulty is mainly due to their non-deterministic pattern of communication and computation. One way to obtain information from the applications is through analysis tools. The PAS2P tool, based on application repeatability, focuses on performance analysis and prediction using the application signature. It uses the same resources that run the parallel application for its analysis, thus creating a machine-independent model and identifying common patterns in the application._x000D_ _x000D_ When applying the PAS2P tool to irregular applications, generating a model is impossible due to the non-homogeneous communication and computation pattern that prevents finding a repeatability pattern, making it difficult to obtain a model that characterizes the behavior of these applications. _x000D_ _x000D_ Due to the above, we present a modeling methodology for irregular parallel applications that analyzes data by processes. This approach successfully characterizes the application based on the behavior of each process. To effectively characterize irregular applications, we group the repeatability patterns of all the processes executing the application into a single model. The model that characterizes the behavior of the irregular parallel application can be used to analyze and predict the execution time of the application on a target machine._x000D_ _x000D_ The experimental validation of the proposed model called "Extension of PAS2P for irregular applications'' demonstrates a reduction in the communications performed by PAS2P, which implies a decrease in the time required for the application analysis. Furthermore, it is possible to characterize irregular applications, establishing a machine-independent model that can predict the execution time with an average error of less than 9%.

Date of Award	4 Apr 2024
Original language	Spanish
Supervisor	Alvaro Wong Gonzalez (Director) & Emilio Luque Fadon (Director)