A Data-driven Analysis of Code Optimizations

2025-11-08

AI Summary

This paper investigates the trade-off between search efficiency and optimization effectiveness in automated code optimization, specifically addressing whether fixed-order application of code transformations can substantially reduce the search space without significantly compromising performance gains. To this end, we propose a data-driven empirical methodology: generating random programs, executing randomized optimization sequences, measuring runtime performance, and constructing a large-scale experimental dataset to statistically characterize inter-transform interactions. Our results demonstrate that several high-performance fixed optimization sequences exist—achieving over 95% of the optimal performance gain while reducing the search space by more than 90% on average. This finding provides empirically validated theoretical support and practical design principles for compiler auto-optimization strategies.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Search
• Machine Learning: Optimization

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

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.

Problem

• Analyzing automatic code optimization transformation behaviors and interactions
• Investigating fixed versus arbitrary transformation order impact on performance
• Developing data-driven insights for efficient automatic optimization algorithm design

Innovation

• Automated code transformation selection without manual intervention
• Fixed transformation sequences to reduce search space
• Data-driven analysis using random programs and optimization sequences