OpenMP is a parallel computing framework that provides programmers with a set of directives and clauses to use when writing parallel applications. The most important task in adopting OpenMP is deciding the parallel pattern with associated clauses to employ in a sequential program that already exists. The shared-memory parallelization is complicated by parallel directives with different roles. Some tools have been developed to assist programmers in developing parallel programs using OpenMP. Many tools, however, have constraints on the size of program analysis, OpenMP scoping, and scalar and array reduction. Manually selecting clauses with the necessary data-sharing attributes is also prone to errors. In this study, we target the variable classification in directives to explore the loop-level parallelism. We set the variable classification problem as a type inference task based on a machine learning method, which understands the attributes of variables in certain contexts and relations. We propose an aligned corpus of tokens and types to predict variable attributes used inside the target loop. We support the reduction clause whenever it is applicable. Experimental results indicate that our method is very promising and favorably suited to dealing with real-world complex programs while showing high accuracy.

OpenMP 是一个并行计算框架，它为程序员提供了一组指令和子句，以便在编写并行应用程序时使用。采用 OpenMP 的最重要任务是决定在已经存在的顺序程序中使用具有关联子句的并行模式。共享内存并行化因具有不同角色的并行指令而变得复杂。已经开发了一些工具来帮助程序员使用 OpenMP 开发并行程序。然而，许多工具对程序分析的大小、OpenMP 范围以及标量和数组缩减都有限制。手动选择具有必要数据共享属性的子句也容易出错。在这项研究中，我们针对指令中的变量分类来探索循环级并行性。我们将变量分类问题设置为基于机器学习方法的类型推理任务，它了解变量在特定上下文和关系中的属性。我们提出了一个对齐的标记和类型语料库来预测目标循环内使用的变量属性。只要适用，我们就支持减免条款。实验结果表明，我们的方法非常有前途，并且非常适合处理现实世界的复杂程序，同时显示出很高的准确性。