未被发现的差异：通过自上而下的差分分析表征编译器优化格局
The Unseen Delta: Characterizing the Compiler Optimization Landscape via Top-Down Differential Analysis
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中文总结 AI 辅助
本研究提出自上而下的差分分析方法，结合微架构指标定位编译器优化导致的性能差异，开发二进制补丁框架修复GCC与Clang生成二进制的性能问题，为优化缺陷分析提供新视角。
中文摘要 AI 辅助
编译器优化是实现现代软件高性能的关键，但近期研究表明性能bug持续存在，这类bug是指编译器生成功能正确但计算效率低下的代码，会导致显著的性能下降。现有检测与测试方法通常采用自下而上的方法，聚焦于特定的低级代码属性，且局限于已知的优化规则，因此难以量化已识别问题的整体影响，还常常忽略关键的微架构低效性。我们观察到未被挖掘潜力的关键指标：相同的源代码经不同编译器编译后生成的二进制文件往往存在显著的性能差异，而这些差异的根本原因在很大程度上仍未被探索，且难以用现有技术精准定位。为填补这一空白，我们提出了自上而下的差分分析方法，该方法通过细粒度的分层微架构指标校准编译器优化差异，提供运行时行为的全面视图。该方法采用基于采样的方法，能高效定位造成性能差异的关键代码片段，支持针对性的根本原因分析。我们的实证评估揭示了GCC与Clang生成的二进制文件之间存在显著且常令人惊讶的性能差异，对根本原因的分类显示出编译器优化中的系统性挑战。为定量验证我们的发现并展示实际影响，我们开发了一个二进制补丁框架，通过从竞争编译器移植更优的代码序列来修复已识别的性能问题。这项工作为理解和分析优化缺陷提供了一种新视角。
英文摘要
Compiler optimizations are essential for achieving high performance in modern software. However, recent studies highlight the persistence of performance bugs, i.e., subtle defects where the compiler generates functionally correct but computationally inefficient code, leading to significant performance degradation. Existing detection and testing methods typically employ a bottom-up approach, focusing on specific low-level code properties and remaining confined to known optimization rules. Consequently, they struggle to quantify the holistic impact of identified issues and often overlook critical microarchitectural inefficiencies. We observe a key indicator of untapped potential: different compilers often produce binaries with significant performance differences for identical source code. However, the root causes of these discrepancies remain largely unexplored and difficult to pinpoint using current techniques. To bridge this gap, we introduce a top-down differential analysis methodology. This approach calibrates compiler optimization differences with fine-grained, hierarchical microarchitectural metrics, offering a comprehensive view of runtime behavior. Using a sampling-based approach, this method efficiently pinpoints the critical code snippets responsible for performance differences, enabling targeted root cause analysis. Our empirical evaluation uncovers substantial and often surprising performance differences between binaries generated by GCC and Clang. A categorization of root causes reveals systemic challenges in compiler optimizations. To quantitatively validate our findings and demonstrate practical impact, we developed a binary patching framework that fixes identified performance issues by transplanting superior code sequences from competing compilers. This work provides a novel lens for understanding and analyzing optimization defects.
发表机构
- State Key Laboratory of Novel Software Technology, Nanjing University（南京大学新型软件技术重点实验室）
- Shandong University（山东大学）
- The Hong Kong University of Science and Technology（香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。
