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Analyzing parallel programs has become increasingly difficult due to the immense amount of information
collected on large systems. In this scenario, cluster analysis has
been proved to be a useful technique to reduce the amount of
data to analyze. A good example is the use of the density-based
cluster algorithm DBSCAN to identify similar single program
multiple data (SPMD) computing phases in message-passing
applications. This structure detection simplifies the analyst
work as the whole information available is reduced to a small
set of clusters.
However, DBSCAN presents two major problems: it is very
sensitive to its parametrization and is not capable of correctly
detect clusters when the data set has different densities across
the data space. In this paper, we introduce the Aggregative
Cluster Refinement, an iterative algorithm that produces more
accurate structure detections of SPMD phases than DBSCAN.
In addition, it is able to detect clusters with different densities
CitationGonzález, J. [et al.]. Automatic refinement of parallel applications structure detection. A: IEEE International Parallel and Distributed Processing Symposium. "2012 IEEE 26th International Parallel and Distributed Processing Symposium workshops & phd forum (IPDPSW)". 2012, p. 1680-1687.
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