The new CUBE beta release includes a very powerful tool for multi-experiment analysis that can be used to compare the effects of different optimization strategies or to integrate data from different monitoring tools.
Since performance tuning of parallel applications usually involves multiple experimental runs to compare the effects of different code versions or to collect data using different monitoring tools, CUBE 2.0b includes a new tool called performance algebra that can be used to merge, subtract, and average the data from different experiments and view the results in the form of a single derived experiment. Using the same representation for derived experiments and original experiments provides access to the derived behavior based on familiar metaphors and tools. In addition, CUBE 2.0b supports online documentation for
performance metrics and includes a converter that allows TAU
users to view their call path profiles with CUBE. For more information please visit the CUBE web page.