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Detailed simulations of large scale message-passing interface parallel applications are extremely time consuming and resource intensive. A new methodology that combines signal processing and data mining techniques plus a multilevel simulation reduces the simulated data by various orders of magnitude. This reduction makes possible detailed software performance analysis and accurate performance predictions...
Measuring the performance of parallel codes is a compromise between lots of factors. The most important one is which data has to be analyzed. Current supercomputers are able to run applications in large number of processors as well as the analysis data that can be extracted is also large and varied. That implies a hard compromise between the potential problems one want to analyze and the information...
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...
Analyzing parallel programs has become increasingly difficult due to the immense amount of information collected on large systems. The use of clustering techniques has been proposed to analyze applications. However, while the objective of previous works is focused on identifying groups of processes with similar characteristics, we target a much finer granularity in the application behavior. In this...
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