Analysis and Visualization of Important Performance Counters to Enhance Interpretability of Autotuner Output
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DescriptionAutotuning is a widely used method for guiding developers of large-scale applications to achieve high performance. However, autotuners typically employ black-box optimizations to recommend parameter settings at the cost of users missing the opportunity to identify performance bottlenecks. Performance analysis fills that gap and identifies problems and optimization opportunities that can result in better runtime and utilization of hardware resources. This work combines the best of the both worlds by integrating a systematic performance analysis and visualization approach into a publicly available autotuning framework, GPTune, to suggest users which configuration parameters are important to tune, to what value, and how tuning the parameters affect hardware-application interactions. Our experiments demonstrate that a subset of the task parameters impact the execution time of the Hypre application; the memory traffic and page faults cause performance problems in the Plasma-DGEMM routine on Cori-Haswell.
