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.
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This scenario also mimics using OpenMP to parallelize the large computation regions.
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Acknowledgements
The work presented in this chapter has been partially founded by IBM, through the IBM-BSC MareIncognito collaboration agreement, the Spanish Ministry of Education under grant BES-2005-7919 and project TIN2007-60625, and the EU/Russia joint project HOPSA.
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Gonzalez, J., Gimenez, J., Labarta, J. (2014). Performance Analytics: Understanding Parallel Applications Using Cluster and Sequence Analysis. In: Knüpfer, A., Gracia, J., Nagel, W., Resch, M. (eds) Tools for High Performance Computing 2013. Springer, Cham. https://doi.org/10.1007/978-3-319-08144-1_1
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