PDCAT: Preference-Driven Compiler Auto-tuning

Compilers are crucial software tools that usually convert programs in high-level languages into machine code. A compiler provides hundreds of optimizations to improve the performance of the compiled code, which are controlled by enabled or disabled optimization flags. However, the vast number of combinations of these flags makes it extremely challenging to select the desired settings for compiler optimization flags (i.e., an optimization sequence) for a given target program. In the literature, many auto-tuning techniques have been proposed to select a desired optimization sequence via different strategies across the entire optimization space. However, due to the huge optimization space, these techniques commonly suffer from the widely recognized efficiency problem. To address this problem, in this paper, we propose a preference-driven selection approach PDCAT, which reduces the search space of optimization sequences through three components. In particular, PDCAT first identifies combined optimizations based on compiler documentation to exclude optimization sequences violating the combined constraints, and then categorizes the optimizations into a common optimization set (whose optimization flags are fixed) and an exploration set containing the remaining optimizations. Finally, within the search process, PDCAT assigns distinct enable probabilities to the explored optimization flags and finally selects a desired optimization sequence. The former two components reduce the search space by removing invalid optimization sequences and fixing some optimization flags, whereas the latter performs a biased search in the search space. To evaluate the performance of the proposed approach PDCAT, we conducted an extensive experimental study on the latest version of the GCC compiler with two widely used benchmarks, cBench and PolyBench. The results show that PDCAT significantly outperforms the four compared techniques, including the state-of-art technique SRTuner. Moreover, each component of PDCAT not only contributes to its performance, but also improves the acceleration performance of the compared techniques.