Collective optimization

Abstract

Iterative optimization is a popular and efficient research approach to optimize programs using feedback-directed compilation. However, one of the key limitations that prevented widespread use in production compilers and day-to-day practice is the necessity to perform a large number of program runs with the same dataset and environment (architecture, OS, compiler) to test many different combinations of optimizations. In this article, we propose to overcome such a practical obstacle using collective optimization , where the task of optimizing a program or tuning default compiler optimization heuristic leverages the experience of many other users continuously, rather than being performed in isolation, and often redundantly, by each user. During this unobtrusive approach, performance information is sent to a central database after each run and statistically combined with the data from all users to suggest most profitable optimizations for a given program and an architecture, or to gradually improve default optimization level of a compiler for a given architecture. In this article, we address two key challenges of collective optimization. We show that it is possible to simultaneously learn and improve performance while avoiding long training phases. We also demonstrate how to use our approach with static compilers to learn optimizations across multiple datasets and architectures without even a reference run normally needed to compute speedups over the baseline optimization by using static function cloning and dynamic adaptation. We present a novel probabilistic approach based on competition among pairs of optimizations (program reaction to optimizations) to enable optimization knowledge reuse and achieve nearly the best possible iterative optimization performance. We implemented our technique in GCC (widespread production open-source compiler that supports multiple architectures) and connected it to a public collective optimization database at cTuning.org to gather profile and optimization data continuously and transparently in realistic environments ranging from desktop PCs and mobile systems to supercomputers and data centers.