Affiliation:
1. Institute of Software Chinese Academy of Sciences, Beijing 100190, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
Abstract
Automatic vectorization is an important technique for compilers to improve the parallelism of programs. With the widespread usage of SIMD (Single Instruction Multiple Data) extensions in modern processors, automatic vectorization has become a hot topic in the research of compiler techniques. Accurately evaluating the effectiveness of automatic vectorization in typical compilers is quite valuable for compiler optimization and design. This paper evaluates the effectiveness of automatic vectorization, analyzes the limitation of automatic vectorization and the main causes, and improves the automatic vectorization technology. This paper firstly classifies the programs by two main factors: program characteristics and transformation methods. Then, it evaluates the effectiveness of automatic vectorization in three well-known compilers (GCC, LLVM, and ICC, including their multiple versions in recent 5 years) through TSVC (Test Suite for Vectorizing Compilers) benchmark. Furthermore, this paper analyzes the limitation of automatic vectorization based on source code analysis, and introduces the differences between academic research and engineering practice in automatic vectorization and the main causes, Finally, it gives some suggestions as to how to improve automatic vectorization capability.
Subject
Computer Science Applications,Software
Reference61 articles.
1. Research on SIMD automatic vectorization compiling optimization;W. Gao;Ruan Jian Xue Bao/Journal Of Software,2015
2. Streaming Simd Extensions[Eb/Ol],2016
3. The ARM Scalable Vector Extension
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Is RISC-V ready for HPC prime-time: Evaluating the 64-core Sophon SG2042 RISC-V CPU;Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis;2023-11-12
2. PHCG: Optimizing Simulink Code Generation for Embedded System With SIMD Instructions;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2023-04