A Data-driven Analysis of Code Optimizations

TL;DR

This paper uses a data-driven approach to analyze how different sequences of automatic code transformations affect performance, aiming to improve compiler optimization strategies efficiently.

Contribution

It introduces a large dataset of randomized program transformations and applies statistical analysis to inform better optimization heuristics.

Findings

Predefined fixed sequences can speed up optimization search.

Random transformation sequences reveal interaction effects.

Data-driven insights guide more efficient optimization algorithms.

Abstract

As the demand for computational power grows, optimizing code through compilers becomes increasingly crucial. In this context, we focus on fully automatic code optimization techniques that automate the process of selecting and applying code transformations for better performance without manual intervention. Understanding how these transformations behave and interact is key to designing more effective optimization strategies. Compiler developers must make numerous design choices when constructing these heuristics. For instance, they may decide whether to allow transformations to be explored in any arbitrary order or to enforce a fixed sequence. While the former may theoretically offer the best performance gains, it significantly increases the search space. This raises an important question: Can a predefined, fixed order of applying transformations speed up the search without severely compromising optimization potential? In this paper, we address this and other related questions that arise in the design of automatic code optimization algorithms. Using a data-driven approach, we generate a large dataset of random programs, apply random optimization sequences, and record their execution times. Through statistical analysis, we provide insights that guide the development of more efficient automatic code optimization algorithms.