  In past years, the world has switched to many-core and multi-core shared
memory architectures. As a result, there is a growing need to utilize these
architectures by introducing shared memory parallelization schemes to software
applications. OpenMP is the most comprehensive API that implements such
schemes, characterized by a readable interface. Nevertheless, introducing
OpenMP into 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 ML techniques, specifically in natural language processing (NLP),
to replace S2S compilers altogether. We create a database (corpus), Open-OMP,
specifically for this goal. Open-OMP contains over 28,000 code snippets, half
of which contain OpenMP directives while the other half do not need
parallelization at all with high probability. We use the corpus to train
systems to automatically classify code segments in need of parallelization, as
well as suggest individual OpenMP clauses. We train several transformer models,
named PragFormer, for these tasks, and show that they outperform
statistically-trained baselines and automatic S2S parallelization compilers in
both classifying the overall need for an OpenMP directive and the introduction
of private and reduction clauses.
  Our source code and database are available at:
https://github.com/Scientific-Computing-Lab-NRCN/PragFormer.
