Hierarchical MATE´s approach for dynamic performance tuning of large-scale parallel applications

Currently, performance analysis support tools are required to exploit the potential performance of large-scale computers. However, in this context, scalability becomes a major problem for this kind of tools. Nowadays, there are automatic performance analysis tools, such as Scalasca [1] or Periscope [2], capable of scaling and looking for performance problems of parallel applications. Nevertheless, if the behaviour of a parallel application varies during the execution according to the data evolution, then dynamic analysis and tuning of the application during its execution, such as that performed by MATE [3] tool, is necessary.