Performance Analytics: Understanding Parallel Applications Using Cluster and Sequence Analysis

Juan Gonzalez (), Judit Gimenez and Jesus Labarta
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Juan Gonzalez: Barcelona Supercomputing Center/Polytechnic University of Catalonia
Judit Gimenez: Barcelona Supercomputing Center/Polytechnic University of Catalonia
Jesus Labarta: Barcelona Supercomputing Center/Polytechnic University of Catalonia

Chapter Chapter 1 in Tools for High Performance Computing 2013, 2014, pp 1-17 from Springer

Abstract: Abstract Due to the increasing complexity of High Performance Computing (HPC) systems and applications it is necessary to maximize the insight of the performance data extracted from an application execution. This is the mission of the Performance Analytics field. In this chapter, we present three different contributions to this field. First, we demonstrate how it is possible to capture the computation structure of parallel applications at fine grain by using density-based clustering algorithms. Second, we introduce the use of multiple sequence alignment algorithms to asses the quality of a computation structure provided by the cluster analysis. Third, we propose a new clustering algorithm to maximize the quality of the computation structure detected minimizing the user intervention. To demonstrate the usefulness of the different techniques, we also present three use cases.

Keywords: Parallel Applications; Performance Analytics Field; Density-based Clustering Algorithm; Structure Detection; Single Process Multiple Data (SPMD) (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-08144-1_1

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DOI: 10.1007/978-3-319-08144-1_1

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