Enhancing Autotuning Capability with a History Database

Abstract

Autotuning is gaining importance to achieve the best possible performance for exascale applications. The performance of an autotuner usually depends on the amount of performance data collected for the application, however, collecting performance data for large-scale applications is oftentimes an expensive and daunting task. This paper presents an autotuner database, which we call a history database, for enhancing the reusability and reproducibility of performance data. The history database is built into a publicly available autotuner called GPTune, and allows users to store performance data obtained from autotuning and download historical performance data provided by the same or other users. The database not only allows reuse of the best available tuning results for widely used codes but also enables transfer learning that can leverage knowledge of pre-trained performance models. An evaluation shows that, for ScaLAPACK's PDGEQRF routine, a transfer learning approach using the history database can attain up to 33% better tuning results compared to single task learning without using prior knowledge, on 2,048 cores of NERSC's Cori supercomputer.