Directive-driven programming models, such as OpenMP, are one solution for exploring the potential parallelism when targeting multicore architectures. Although these approaches significantly help developers, code parallelization is still a non-trivial and time-consuming process, requiring parallel programming skills. Thus, many efforts have been made toward automatic parallelization of the existing sequential code. This article presents AutoPar-Clava, an OpenMP-based automatic parallelization compiler which: (1) statically detects parallelizable loops in C applications; (2) classifies variables used inside the target loop based on their access pattern; (3) supports reduction clauses on scalar and array variables whenever it is applicable; and (4) generates a C OpenMP parallel code from the input sequential version. The effectiveness of AutoPar-Clava is evaluated by using the NAS and Polyhedral Benchmark suites and targeting a x86-based computing platform. The achieved results are very promising and compare favorably with closely related auto-parallelization compilers, such as Intel C/C++ Compiler (icc), ROSE, TRACO and CETUS.

中文翻译：

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针对基于 OpenMP 的 C 应用程序自动并行化的源到源编译

指令驱动的编程模型（例如 OpenMP）是一种解决方案，用于在面向多核架构时探索潜在的并行性。尽管这些方法对开发人员有很大帮助，但代码并行化仍然是一个重要且耗时的过程，需要并行编程技能。因此，已经为现有顺序代码的自动并行化做出了许多努力。本文介绍 AutoPar-Clava，一种基于 OpenMP 的自动并行化编译器，它： (1) 静态检测 C 应用程序中的可并行化循环；(2) 根据访问模式对目标循环中使用的变量进行分类；(3) 在适用时支持标量和数组变量的归约子句；(4) 根据输入的顺序版本生成 C OpenMP 并行代码。AutoPar-Clava 的有效性通过使用 NAS 和 Polyhedral Benchmark 套件并针对基于 x86 的计算平台进行评估。取得的结果非常有希望，与密切相关的自动并行化编译器（如 Intel C/C++ Compiler (icc)、ROSE、TRACO 和 CETUS）相比毫不逊色。