Quantifying OpenMP: Statistical Insights into Usage and Adoption

Tal Kadosh; Niranjan Hasabnis; Timothy Mattson; Yuval Pinter; Gal Oren

doi:10.1109/hpec58863.2023.10363459

Quantifying OpenMP: Statistical Insights into Usage and Adoption

Tal Kadosh, Niranjan Hasabnis, Timothy Mattson, Yuval Pinter, Gal Oren

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

In high-performance computing (HPC), the demand for efficient parallel programming models has grown dramatically since the end of Dennard Scaling and the subsequent move to multi-core CPUs. OpenMP stands out as a popular choice due to its simplicity and portability, offering a directive-driven approach for shared-memory parallel programming. Despite its wide adoption, however, there is a lack of comprehensive data on the actual usage of OpenMP constructs, hindering unbiased insights into its popularity and evolution. This paper presents a statistical analysis of OpenMP usage and adoption trends based on a novel and extensive database, HPCORPUS, compiled from GitHub repositories containing C, C++, and Fortran code. The results reveal that OpenMP is the dominant parallel programming model, accounting for 45% of all analyzed parallel APIs. Furthermore, it has demonstrated steady and continuous growth in popularity over the past decade. Analyzing specific OpenMP constructs, the study provides in-depth insights into their usage patterns and preferences across the three languages. Notably, we found that while OpenMP has a strong 'common core' of constructs in common usage (while the rest of the API is less used), there are new adoption trends as well, such as simd and target directives for accelerated computing and task for irregular parallelism. Overall, this study sheds light on OpenMP's significance in HPC applications and provides valuable data for researchers and practitioners. It showcases OpenMP's versatility, evolving adoption, and relevance in contemporary parallel programming, underlining its continued role in HPC applications and beyond. These statistical insights are essential for making informed decisions about parallelization strategies and provide a foundation for further advancements in parallel programming models and techniques. HPCORPUS, as well as the analysis scripts and raw results, are available at: https://github.com/Scientific-Computing-Lab-NRCN/HPCorpus

Original language	English
Title of host publication	2023 IEEE High Performance Extreme Computing Conference, HPEC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350308600
DOIs	https://doi.org/10.1109/hpec58863.2023.10363459
State	Published - 2023
Externally published	Yes
Event	2023 IEEE High Performance Extreme Computing Conference, HPEC 2023 - Virtual, Online, United States Duration: 25 Sep 2023 → 29 Sep 2023

Publication series

Name	2023 IEEE High Performance Extreme Computing Conference, HPEC 2023

Conference

Conference	2023 IEEE High Performance Extreme Computing Conference, HPEC 2023
Country/Territory	United States
City	Virtual, Online
Period	25/09/23 → 29/09/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Funding

This research was supported by the Israeli Council for Higher Education (CHE) via the Data Science Research Center, Ben- Gurion University of the Negev, Israel; Pazy grant 226/20; Intel Corporation (oneAPI CoE program); and the Lynn and William Frankel Center for Computer Science. Computational support was provided by the NegevHPC project [44], Intel Developer Cloud [45], and Google Cloud Platform (GCP). The authors thank Re em Harel, Yehonatan Fridman, Israel Hen, and Gabi Dadush for their help and support. This research was supported by the Israeli Council for Higher Education (CHE) via the Data Science Research Center, Ben-Gurion University of the Negev, Israel; Pazy grant 226/20; Intel Corporation (oneAPI CoE program); and the Lynn and William Frankel Center for Computer Science. Computational support was provided by the NegevHPC project [44], Intel Developer Cloud [45], and Google Cloud Platform (GCP). The authors thank Re’em Harel, Yehonatan Fridman, Israel Hen, and Gabi Dadush for their help and support.

Funders	Funder number
Data Science Research Center
Google Cloud Platform
Gurion University of the Negev, Israel
Intel Developer Cloud
Lynn and William Frankel Center for Computer Science
Intel Corporation
Ben-Gurion University of the Negev	226/20
Council for Higher Education

Keywords

BigQuery
C
C++
CUDA
Cilk
Fortran
GitHub
HPCorpus
MPI
OpenACC
OpenCL
OpenMP
SYCL
TBB

Access to Document

10.1109/hpec58863.2023.10363459

Cite this

Kadosh, T., Hasabnis, N., Mattson, T., Pinter, Y., & Oren, G. (2023). Quantifying OpenMP: Statistical Insights into Usage and Adoption. In 2023 IEEE High Performance Extreme Computing Conference, HPEC 2023 (2023 IEEE High Performance Extreme Computing Conference, HPEC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/hpec58863.2023.10363459

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abstract = "In high-performance computing (HPC), the demand for efficient parallel programming models has grown dramatically since the end of Dennard Scaling and the subsequent move to multi-core CPUs. OpenMP stands out as a popular choice due to its simplicity and portability, offering a directive-driven approach for shared-memory parallel programming. Despite its wide adoption, however, there is a lack of comprehensive data on the actual usage of OpenMP constructs, hindering unbiased insights into its popularity and evolution. This paper presents a statistical analysis of OpenMP usage and adoption trends based on a novel and extensive database, HPCORPUS, compiled from GitHub repositories containing C, C++, and Fortran code. The results reveal that OpenMP is the dominant parallel programming model, accounting for 45% of all analyzed parallel APIs. Furthermore, it has demonstrated steady and continuous growth in popularity over the past decade. Analyzing specific OpenMP constructs, the study provides in-depth insights into their usage patterns and preferences across the three languages. Notably, we found that while OpenMP has a strong 'common core' of constructs in common usage (while the rest of the API is less used), there are new adoption trends as well, such as simd and target directives for accelerated computing and task for irregular parallelism. Overall, this study sheds light on OpenMP's significance in HPC applications and provides valuable data for researchers and practitioners. It showcases OpenMP's versatility, evolving adoption, and relevance in contemporary parallel programming, underlining its continued role in HPC applications and beyond. These statistical insights are essential for making informed decisions about parallelization strategies and provide a foundation for further advancements in parallel programming models and techniques. HPCORPUS, as well as the analysis scripts and raw results, are available at: https://github.com/Scientific-Computing-Lab-NRCN/HPCorpus",

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Kadosh, T, Hasabnis, N, Mattson, T, Pinter, Y & Oren, G 2023, Quantifying OpenMP: Statistical Insights into Usage and Adoption. in 2023 IEEE High Performance Extreme Computing Conference, HPEC 2023. 2023 IEEE High Performance Extreme Computing Conference, HPEC 2023, Institute of Electrical and Electronics Engineers Inc., 2023 IEEE High Performance Extreme Computing Conference, HPEC 2023, Virtual, Online, United States, 25/09/23. https://doi.org/10.1109/hpec58863.2023.10363459

Quantifying OpenMP: Statistical Insights into Usage and Adoption. / Kadosh, Tal; Hasabnis, Niranjan; Mattson, Timothy et al.
2023 IEEE High Performance Extreme Computing Conference, HPEC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE High Performance Extreme Computing Conference, HPEC 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Quantifying OpenMP: Statistical Insights into Usage and Adoption

Abstract

Publication series

Conference

Bibliographical note

Funding

Keywords

Access to Document

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Cite this