# Quantifying OpenMP: Statistical Insights into Usage and Adoption

arXiv: https://arxiv.org/abs/2308.08002 · HTML: https://mlsanity.com/paper/2308.08002/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2023.

## Built on by (most cited first, 9 of 9)

- [The Landscape and Challenges of HPC Research and LLMs](https://mlsanity.com/paper/2402.02018/) (2024)
- [OMPGPT: A Generative Pre-trained Transformer Model for OpenMP](https://mlsanity.com/paper/2401.16445/) (2024)
- [Scope is all you need: Transforming LLMs for HPC Code](https://mlsanity.com/paper/2308.09440/) (2023)
- [MonoCoder: Domain-Specific Code Language Model for HPC Codes and Tasks](https://mlsanity.com/paper/2312.13322/) (2023)
- [MPIrigen: MPI Code Generation through Domain-Specific Language Models](https://mlsanity.com/paper/2402.09126/) (2024)
- [MIREncoder: Multi-modal IR-based Pretrained Embeddings for Performance Optimizations](https://mlsanity.com/paper/2407.02238/) (2024)
- [ACCeLLiuM: Supervised Fine-Tuning for Automated OpenACC Pragma Generation](https://mlsanity.com/paper/2509.20380/) (2025)
- [ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation](https://mlsanity.com/paper/2601.04327/) (2026)
- [Detecting Soft Errors in Parallel Software with LLM-tuned Instruction Duplication](https://mlsanity.com/paper/2609.19531/) (2026)

## Abstract (arXiv)

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.