Exploiting Pure Superword Level Parallelism for Array Indirections

Sun, Hongwei; Zhao, Rongcai; Gao, Wei; Gong, Yi; Li, Gang

doi:10.1109/paap.2015.14

Cited by 1 publication

(1 citation statement)

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“…A solution to this type of problem is introduced in Section 5. (2) Automatic vectorization of indirect array access: Indirect array [54]is a programwhereinthe index of the exponent group is obtained indirectly through array access valuesor array access operations. For example,Figure 7is [i]] is an indirect array access, and access to the content of arrayb requiresidxretrieval.…”

Section: Typical Programmentioning

confidence: 99%

Evaluation of Compilers’ Capability of Automatic Vectorization Based on Source Code Analysis

Feng

Tao

2021

Scientific Programming

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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.

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Section: Typical Programmentioning

confidence: 99%