The Taub Faculty of Computer Science Events and Talks

Introducing parallelism to applications is a complex and tedious task. As a result, the field named automatic parallelization emerged. Automatic parallelization refers to the seamless introduction of parallel schemes (such as OpenMP directives) to code. In other words, creating a tool that will mimic the human comprehension process to insert parallelization schemes. In the recent past, the main focus of this field was on creating deterministic tools such as specific functionality embedded in compilers and dedicated source-to-source (S2S) compilers. However, recent advances and success in deep Natural Language Processing (NLP) inspired models for similar code-related tasks. For example, Codex (based on GPT) generates and suggests code. The possibility of creating a similar model for automatically introducing, or at the very least suggesting, OpenMP directives rises. In this talk, we will go through this field's history and the state-of-the-art - from the deterministic/algorithmic approach to the Machine Learning one, based on our recent publications. References: ● Harel, R. E., Mosseri, I., Levin, H., Alon, L. O., Rusanovsky, M., & Oren, G. (2020). Source-to-source parallelization compilers for scientific shared-memory multi-core and accelerated multiprocessing: analysis, pitfalls, enhancement and potential. International Journal of Parallel Programming, 48(1), 1-31. ● Mosseri, I., Alon, L. O., Harel, R. E., & Oren, G. (2020, September). ComPar: optimized multi-compiler for automatic OpenMP S2S parallelization. In International Workshop on OpenMP (pp. 247-262). Springer, Cham. ● Harel, R. E., Pinter, Y., & Oren, G. (2022). Learning to Parallelize in a Shared-Memory Environment with Transformers. arXiv preprint arXiv:2204.12835. Extended Abstract: The International Conference for High-Performance Computing, Networking, Storage, and Analysis (SC 2022) Biography: Re’em Harel is a computer science Ph.D. student at Ben-Gurion University and an oneAPI student ambassador. The main focus of his Ph.D. research is using state-of-the-art NLP models to automatically introduce parallelization schemes, such as OpenMP directives and MPI functions, to new and legacy codes. In addition, he is a researcher in the scientific computing lab at NRCN, focusing on parallel programming and scientific computing.