Learning to Parallelize in a Shared-Memory Environment with Transformers

Abstract

In past years, the world has switched to multi and many core shared memory architectures. As a result, there is a growing need to utilize these architectures by introducing shared memory parallelization schemes, such as OpenMP, to applications. Nevertheless, introducing OpenMP work-sharing loop construct into code, especially legacy code, is challenging due to pervasive pitfalls in management of parallel shared memory. To facilitate the performance of this task, many source-to-source (S2S) compilers have been created over the years, tasked with inserting OpenMP directives into code automatically. In addition to having limited robustness to their input format, these compilers still do not achieve satisfactory coverage and precision in locating parallelizable code and generating appropriate directives. In this work, we propose leveraging recent advances in machine learning techniques, specifically in natural language processing (NLP), to suggest the need for an OpenMP work-sharing loop directive and data-sharing attributes clauses --- the building blocks of concurrent programming. We train several transformer models, named PragFormer, for these tasks and show that they outperform statistically-trained baselines and automatic source-to-source (S2S) parallelization compilers in both classifying the overall need for an parallel for directive and the introduction of private and reduction clauses. In the future, our corpus can be used for additional tasks, up to generating entire OpenMP directives. The source code and database for our project can be accessed on GitHub 1 and HuggingFace 2.