In advancing parallel programming, particularly with OpenMP, the shift towards NLP-based methods marks a significant innovation beyond traditional S2S tools like Autopar and Cetus. These NLP approaches train on extensive datasets of examples to efficiently generate optimized parallel code, streamlining the development process. This method's strength lies in its ability to swiftly produce parallelized code that runs efficiently. However, this reliance on NLP models, without direct code analysis, can introduce inaccuracies, as these models might not fully grasp the nuanced semantics of the code they parallelize. We build OMP-Engineer, which balances the efficiency and scalability of NLP models with the accuracy and reliability of traditional methods, aiming to enhance the performance of automating parallelization while navigating its inherent challenges.

翻译：在推进并行编程特别是OpenMP方面，向基于NLP方法的转变标志着超越传统S2S工具（如Autopar和Cetus）的重要创新。这些NLP方法通过大规模示例数据集训练，高效生成优化的并行代码，简化了开发流程。该方法优势在于能快速产出高效运行的并行化代码。然而，这种依赖NLP模型而缺乏直接代码分析的方式可能引入不准确性，因为模型可能无法完全理解所并行化代码的细微语义。我们构建了OMP-Engineer，它在NLP模型的效率与可扩展性以及传统方法的准确性与可靠性之间取得平衡，旨在提升自动化并行化的性能，同时应对其固有挑战。