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Modified condition/decision coverage(MC/DC)oriented compiler optimization for symbolic execution

Frontiers of Information Technology & Electronic Engineering 2020年第9期21卷 1267-1284页

作者： Wei-jiang HONG Yi-jun LIU Zhen-bang CHEN Wei DONG Ji WANG College of Computer National University of Defense TechnologyChangsha 410073China State Key Laboratory of High Performance Computing National University of Defense TechnologyChangsha 410073China

Symbolic execution is an effective way of systematically exploring the search space of a program,and is often used for automatic software testing and bug *** program to be analyzed is usually compiled into a binary or an intermediate representation,on which symbolic execution is carried *** this process,compiler optimizations influence the effectiveness and efficiency of symbolic ***,to the best of our knowledge,there exists no work on compiler optimization recommendation for symbolic execution with respect to(w.r.t.)modified condition/decision coverage(MC/DC),which is an important testing coverage criterion widely used for mission-critical *** study describes our use of a state-of-the-art symbolic execution tool to carry out extensive experiments to study the impact of compiler optimizations on symbolic execution ***/*** results indicate that instruction combining(IC)optimization is the important and dominant optimization for symbolic execution ***/*** designed and implemented a support vector machine based optimization recommendation method ***(denoted as auto).The experiments on two standard benchmarks(Coreutils and NECLA)showed that auto achieves the best MC/DC on 67.47%of Coreutils programs and 78.26%of NECLA programs.

关键词： compiler optimization Modified condition/decision coverage(MC/DC) optimization recommendation Symbolic execution

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Boosting compiler Testing via compiler optimization Exploration

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2022年第4期31卷 72-72页

作者： Chen, Junjie Suo, Chenyao Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China

compilers are a kind of important software, and similar to the quality assurance of other software, compiler testing is one of the most widely-used ways of guaranteeing their quality. compiler bugs tend to occur in compiler optimizations. Detecting optimization bugs needs to consider two main factors: (1) the optimization flags controlling the accessability of the compiler buggy code should be turned on;and (2) the test program should be able to trigger the buggy code. However, existing compiler testing approaches only consider the latter to generate effective test programs, but just run them under several pre-defined optimization levels (e.g., -O0, -O1, -O2, -O3, -Os in GCC). To better understand the influence of compiler optimizations on compiler testing, we conduct the first empirical study, and find that (1) all the bugs detected under the widely-used optimization levels are also detected under the explored optimization settings (we call a combination of optimization flags turned on for compilation an optimization setting), while 83.54% of bugs are only detected under the latter;(2) there exist both inhibition effect and promotion effect among optimization flags for compiler testing, indicating the necessity and challenges of considering the factor of compiler optimizations in compiler testing. We then propose the first approach, called COTest, by considering both factors to test compilers. Specifically, COTest first adopts machine-learning (the XGBoost algorithm) to model the relationship between test programs and optimization settings, to predict the bug-triggering probability of a test program under an optimization setting. Then, it designs a diversity augmentation strategy to select a set of diverse candidate optimization settings for prediction for a test program. Finally, Top-K optimization settings are selected for compiler testing according to the predicted bug-triggering probabilities. The experiments on GCC and LLVM demonstrate its effectiveness, espec

关键词： compiler testing compiler optimization machine learning

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RECURSION ANALYSIS FOR compiler optimization

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COMMUNICATIONS OF THE ACM 1976年第9期19卷 514-516页

作者： WALTER, KG CASE WESTERN RESERVE UNIV DEPT COMP & INFORMATION SCICLEVELANDOH 44106

A relatively simple method for the detection of recursive use of procedures is presented for use in compiler optimization. Implementation considerations are discussed, and a modification of the algorithm is given to further improve optimization. This analysis can also be used to determine what possibile subset of values could be assumed by variables which can only take on a relatively small discrete set of values. The most common are parameters of variables assuming values of label, procedure, or Pascal's enumerated type. [ABSTRACT FROM AUTHOR]

关键词： compiler optimization recursion

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MC/DC coverage-oriented compiler optimization for symbolic execution 18

MC/DC coverage-oriented compiler optimization for symbolic e...

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Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings

作者： Yijun Liu Zhenbang Chen Wei Dong Chendong Feng National University of Defense Technology Changsha China

ISBN: (纸本)9781450356633

compiler optimizations influence the effectiveness and efficiency of symbolic execution. In this extended abstract, we report our recent results of recommending compiler optimizations for symbolic execution w.r.t. MC/DC coverage. We carried out extensive experiments to study the influence of compiler optimizations on MC/DC coverage. Then, an SVM-based optimization recommendation method is designed and implementation. The preliminary experimental results are promising.

关键词： symbolic execution compiler optimization recommendation

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The Influences of compiler optimization on Binary Files Similarity Detection

The Influences of Compiler Optimization on Binary Files Simi...

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2013教育技术与信息系统国际会议

作者： Hui Chen School of Computer Science and Technology Shandong Yingcai University

Binary files are generated after compilation. To analyze the similarity detection of binary files, the influences of compiler optimization should be considered. We tested generated binary files with similarity detection tools through the reclassification of clone types and the utility of different compiler optimization. The results show that the similarity detection is less influenced when benchmark files and object files adopting the same compiler optimization;and declined when adopting the different compiler optimization;in addition, the influence of compiler optimization is slightly different according to various clone types.

关键词： Clone Detection compiler optimization Binary File Comparison

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Machine Learning-Driven GCC Loop Unrolling optimization: compiler Performance Enhancement Strategy Based on XGBoost

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2025年第1期34卷

作者： Shi, Zhaoyi Gao, Jun Guan, Xin Tianjin Univ Sch Microelect Tianjin 300072 Peoples R China Phytium Technol Co Ltd Processor Core Res & Dev Dept Changsha 410073 Hunan Peoples R China

In contemporary compilers, the determination of the loop unrolling factor is traditionally based on manually crafted heuristic rules. This approach heavily relies on human intuition, which limits its ability to achieve optimized performance across diverse architectures and can sometimes even lead to performance declines. Additionally, developers face challenges in achieving cross-platform compatibility, often necessitating extensive redesign efforts. In response, this study introduces a method leveraging the XGBoost algorithm to predict the optimal loop unrolling factor for compiler optimization, thereby aiming to replace human thinking with machine learning methods and standardize development processes. Initially, the study gathers data on the loop unrolling factors as determined by profile guided optimization technology, analyzes program-specific loop feature vectors and employs cross-validation, including the Pearson correlation coefficient and feature importance ranking, to construct a dataset. Subsequent use of XGBoost to train this dataset models the decision-making process for selecting the most effective loop unrolling factor. The final step involves integrating XGBoost's trained decision tree model into GCC to calculate the optimal loop unrolling factor during actual compilation. Empirical results on the RISC-V platform indicate that this new method, when tested against the SPEC CPU 2006 benchmark suite, offers up to 6.18% improvement in performance over the existing heuristic approach. It provides a new method for loop unrolling in compilers, and provides an innovative guide for the application of machine learning in compilers.

关键词： Loop unrolling XGBoost compiler optimization feature extraction profile guided optimization

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VISOPT - Visualization of compiler optimizations for Computer Science Education 2025

VISOPT - Visualization of Compiler Optimizations for Compute...

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56th Technical Symposium on Computer Science Education

作者： Koitz-Hristov, Roxane Mandl, Franz Wotawa, Franz Graz Univ Technol Inst Software Technol Graz Austria

ISBN: (纸本)9798400705328

Visualizations in teaching have become a common practice as they effectively convey theoretical concepts. compiler construction, a heavily theory-based subject in computer science education, is particularly challenging for students to understand. While many tools simulate a compiler's front end, or analysis phase, applications that focus on the back end, or synthesis phase, are scarce. This paper describes VisOpt, a web-based visualization tool designed for a master's level compiler Construction course. VisOpt focuses on the synthesis phase, i.e., code optimization and code generation. Its primary objective is to help students comprehend various local compiler optimizations, which can be visualized on the original code, an intermediate representation, or an assembler-like target code. A quasi-experiment with a pre-test-post-test design revealed that students who used VisOpt reported higher self-efficacy compared to those who did not. Although no significant improvement in learning outcomes was observed overall, we propose VisOpt as an engaging pedagogical tool that effectively complements traditional methods for teaching the synthesis phase of compilers.

关键词： compiler optimization Visualization Simulation software compiler Visualization Computer Science Education

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compiler and runtime techniques for software transactional memory optimization

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2009年第1期21卷 7-23页

作者： Wu, Peng Michael, Maged M. von Praun, Christoph Nakalke, Takuya Bordawekar, Rajesh Cain, Harold W. Cascaval, Calin Chatterjee, Siddhartha Chiras, Stefame Hou, Rui Mergen, Mark Shen, Xiaowei Spear, Michael F. Wang, Hua Yong Wang, Kun IBM Res Corp Yorktown Hts NY USA Univ Rochester Rochester NY USA

Software transactional memory (STM) systems are an attractive environment to evaluate optimistic concurrency. We describe our experience of supporting and optimizing an STM system at both the managed runtime and compiler levels. We describe the design policies of our STM system and the statistics collected by the runtime to identify performance bottlenecks and guide tuning decisions. We present an initial work on supporting automatic instrumentation of the STM primitives for C/C++ and Java programs in the IBM XL compiler and J9 Java virtual machine. We evaluate and discuss the performance of several transactional programs running on our system. Copyright (c) 2008 John Wiley & Sons, Ltd.

关键词： transactional memory software transactional memory compiler optimization optimistic concurrency

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Multiple Function Merging for Code Size Reduction

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ACM TRANSACTIONS ON ARCHITECTURE AND CODE optimization 2025年第1期22卷 1-26页

作者： Saito, Yuta Sakamoto, Kazunori Washizaki, Hironori Fukazawa, Yoshiaki Waseda Univ Shinju Ku Tokyo Japan

Resource-constrained environments, such as embedded devices, have limited amounts of memory and storage. Practical programming languages such as C++ and Rust tend to output multiple similar functions by monomorphizing polymorphic functions. An optimization technique called Function Merging, which merges similar functions into a single function, has been studied. However, in the state-of-the-art approach, the number of functions that can be merged at once is limited to two;thus, efficiently merging three or more functions, which are often generated from polymorphic functions, has been impossible. In this study, we propose Multiple Function Merging optimization, which targets merging three or more similar functions into a single function using a multiple sequence alignment algorithm. With multiple aligned information, Multiple Function Merging can increase merge opportunities and reduce extra branching overheads at the code generation stage. We evaluated it using the SPEC CPU benchmark suite and some large-scale C/C++ programs, and the results show that it reduces code size by as much as 7.61% compared with the state-of-the-art approach.

关键词： LLVM compiler optimization code size reduction function merging

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Efficient and Fast High-Performance Library Generation for Deep Learning Accelerators

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IEEE TRANSACTIONS ON COMPUTERS 2025年第1期74卷 155-169页

作者： Bi, Jun Wen, Yuanbo Li, Xiaqing Zhao, Yongwei Guo, Yuxuan Zhou, Enshuai Hu, Xing Du, Zidong Li, Ling Chen, Huaping Chen, Tianshi Guo, Qi Chinese Acad Sci Inst Comp Technol State Key Lab Processors Beijing 100864 Peoples R China Shanghai Innovat Ctr Processor Technol SH Shanghai 200000 Peoples R China Univ Sci & Technol China Hefei 230052 Anhui Peoples R China Cambricon Technol Beijing 100191 Peoples R China Chinese Acad Sci Inst Software Intelligent Software Res Ctr Beijing 100190 Peoples R China

The widespread adoption of deep learning accelerators (DLAs) underscores their pivotal role in improving the performance and energy efficiency of neural networks. To fully leverage the capabilities of these accelerators, exploration-based library generation approaches have been widely used to substantially reduce software development overhead. However, these approaches have been challenged by issues related to sub-optimal optimization results and excessive optimization overheads. In this paper, we propose Heron to generate high-performance libraries of DLAs in an efficient and fast way. The key is automatically enforcing massive constraints through the entire program generation process and guiding the exploration with an accurate pre-trained cost model. Heron represents the search space as a constrained satisfaction problem (CSP) and explores the space via evolving the CSPs. Thus, the sophisticated constraints of the search space are strictly preserved during the entire exploration process. The exploration algorithm has the flexibility to engage in space exploration using either online-trained models or pre-trained models. Experimental results demonstrate that Heron averagely achieves 2.71$\times$x speedup over three state-of-the-art automatic generation approaches. Also, compared to vendor-provided hand-tuned libraries, Heron achieves a 2.00$\times$x speedup on average. When employing a pre-trained model, Heron achieves 11.6$\times$x compilation time speedup, incurring a minor impact on execution time.

关键词： optimization Space exploration Schedules Libraries Biological cells Deep learning Costs Computers Search problems Tensors Code generation compiler optimization tensor computation

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