Team Camus

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Section: Software

NLR

We have developed a program implementing our loop-nest recognition algorithm, detailed in [7] . This standalone, filter-like application takes as input a raw trace and builds a sequence of loop nests that, when executed, reproduce the trace. It is also able to predict forthcoming values at an arbitrary distance in the future. Its simple, text-based input format makes it applicable to all kinds of data. These data can take the form of simple numeric values, or have more elaborate structure, and can include symbols. The program is written is standard ANSI C. The code can also be used as a library.

We have used this code to evaluate the compression potential of loop nest recognition on memory address traces, with very good results. We have also shown that the predictive power of our model is competitive with other models on average. The software is available upon request to anybody interested in trying to apply loop nest recognition. It has been distributed to a dozen of colleagues around the world.

We plan on using this software as the base for a new tool we currently design, for the analysis of parallel traces.