Visualizing More Performance Data Than What Fits on Your Screen

Lucas M. Schnorr () and Arnaud Legrand
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Lucas M. Schnorr: INRIA MESCAL Research Team, CNRS LIG Laboratory
Arnaud Legrand: INRIA MESCAL Research Team, CNRS LIG Laboratory

A chapter in Tools for High Performance Computing 2012, 2013, pp 149-162 from Springer

Abstract: Abstract High performance applications are composed of many processes that are executed in large-scale systems with possibly millions of computing units. A possible way to conduct a performance analysis of such applications is to register in trace files the behavior of all processes belonging to the same application. The large number of processes and the very detailed behavior that we can record about them lead to a trace size explosion both in space and time dimensions. The performance visualization of such data is very challenging because of the quantities involved and the limited screen space available to draw them all. If the amount of data is not properly treated for visualization, the analysis may give the wrong idea about the behavior registered in the traces. This paper is twofold: first, it details data aggregation techniques that are fully configurable by the user to control the level of details in both space and time dimensions; second, it presents two visualization techniques that take advantage of the aggregated data to scale. These features are part of the Viva open-source tool and framework, which is also briefly described in this paper.

Keywords: Data Aggregation; Visualization Tool; Visualization Technique; Spatial Aggregation; Aggregation Algorithm (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-37349-7_10

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DOI: 10.1007/978-3-642-37349-7_10

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