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Started in February.2006
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Collective Benchmark with MiDatasets enables realistic program and architecture optimizations, run-time adaptation and performance evaluation.
Iterative optimization is now a popular technique to obtain performance or code size improvements over the default settings in a compiler. However, in most of the research projects, the best configuration is found for one arbitrary dataset and it is assumed that this configuration will work well with any other dataset that a program uses. We created 20 different datasets per program for MiBench benchmark to evaluate this assumption and analyze the behavior of various programs with multiple datasets.