Tools for machine-learning-based empirical autotuning and specialization

Abstract

The process of empirical autotuning results in the generation of many code variants which are tested, found to be suboptimal, and discarded. By retaining annotated performance profiles of each variant tested over the course of many autotuning runs of the same code across different hardware environments and different input datasets, we can apply machine learning algorithms to generate classifiers for runtime selection of code variants from a library, generate specialized variants, and potentially speed the process of autotuning by starting the search from a point predicted to be close to optimal. In this paper, we show how the TAU Performance System suite of tools can be applied to autotuning to enable reuse of performance data generated through autotuning.