Performance analysis of HPC applications on low-power embedded platforms
Proceedings of the Conference on Design, Automation and Test in Europe
On the usefulness of object tracking techniques in performance analysis
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Framework for a productive performance optimization
Parallel Computing
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Many data mining techniques have been proposed for parallel applications performance analysis, the most interesting being clustering analysis. Most cases have been used to detect processors with similar behavior. In previous work, we presented a different approach: clustering was used to detect the computation structure of the applications and how these different computation phases behave. In this paper, we present a method to evaluate the accuracy of this structure detection. This new method is based on the Single Program Multiple Data (SPMD) paradigm exhibited by real parallel programs. Assuming an SPMD structure, we expect that all tasks of a parallel application execute the same operation sequence. Using a Multiple Sequence Alignment (MSA) algorithm, we check the sequence ordering of the detected clusters to evaluate the quality of the clustering results.