CAREER: A Framework for Dynamic Self-Tuning of General Purpose Programs

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CAREER: A Framework for Dynamic Self-Tuning of General Purpose ProgramsAbstractAs computing systems become more complicated, they are exposing an increasingly large number of "knobs" that can be used for tuning. These knobs typically represent a trade-off (e.g., size vs. speed of a local data storage resource) and thus must be set differently for different workloads to achieve optimal performance (or power-performance). While many such knobs have been proposed, there has been little work towards a comprehensive approach to set these knobs automatically. The proposed research is meant to help fill this gap.Specifically, this work proposes a dynamic self-tuning framework for optimizing the compilation of general-purpose programs. The proposed framework draws inspiration from the successes in empirical optimization frameworks like ATLAS, applying their ideas to a new context where optimization cannot be done at install time. By performing the tuning at run time, code can be optimized for specific input data (a necessity for non-numeric programs), but introduces challenges in maintaining low overhead and good performance in the presence on non-stationary workloads.