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Profiling techniques are used extensively at different parts of the computingstack to achieve many goals. One major goal is to make a piece of softwareexecute more efficiently on a specific hardware platform, where efficiencyspans criteria such as power, performance, resource requirements, etc.Researchers, both in academia and industry, have introduced many techniques togather, and make use of, profiling data. However, one thing remains unchanged:making application A run more efficiently on machine 1. In this paper, weextend this criteria by asking: can profiling information of application A onmachine 1 be used to make application B run more efficiently on machine 1? Ifso, then this means as machine 1 continues to execute more applications, itbecomes better and more efficient. We present a generalized method for usingprofiling information gathered from the execution of programs from a limitedcorpus of applications to improve the performance of software from outside ourcorpus. As a proof of concept, we apply our technique to the specific problemof selecting the most efficient last-level-cache with which to execute anapplication. We were able to turn off an average of 19% of last-level-cacheblocks for selected programs from PARSEC benchmark suite and only saw anaverage 2.8% increase in the rate of last-level cache misses.

Beyond Profiling: Scaling Profiling Data Usage to Multiple Applications