# MPIrigen: MPI Code Generation through Domain-Specific Language Models

arXiv: https://arxiv.org/abs/2402.09126 · HTML: https://mlsanity.com/paper/2402.09126/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 14 Feb 2024.

## Builds on (most cited first, 11 of 11)

- [Can Large Language Models Write Parallel Code?](https://mlsanity.com/paper/2401.12554/) (2024)
- [Comparing Llama-2 and GPT-3 LLMs for HPC kernels generation](https://mlsanity.com/paper/2309.07103/) (2023)
- [Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation](https://mlsanity.com/paper/2306.15121/) (2023)
- [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)
- [Learning to Parallelize in a Shared-Memory Environment with Transformers](https://mlsanity.com/paper/2204.12835/) (2022)
- [Advising OpenMP Parallelization via a Graph-Based Approach with Transformers](https://mlsanity.com/paper/2305.11999/) (2023)
- [Quantifying OpenMP: Statistical Insights into Usage and Adoption](https://mlsanity.com/paper/2308.08002/) (2023)
- [ComPar: Optimized Multi-Compiler for Automatic OpenMP S2S Parallelization](https://mlsanity.com/paper/2005.13304/) (2020)
- [MPI-rical: Data-Driven MPI Distributed Parallelism Assistance with Transformers](https://mlsanity.com/paper/2305.09438/) (2023)
- [OMP2MPI: Automatic MPI code generation from OpenMP programs](https://mlsanity.com/paper/1502.02921/) (2015)

## Built on by (most cited first, 5 of 5)

- [HPC-Coder-V2: Studying Code LLMs Across Low-Resource Parallel Languages](https://mlsanity.com/paper/2412.15178/) (2024)
- [A Structure-Aware Framework for Learning Device Placements on Computation Graphs](https://mlsanity.com/paper/2405.14185/) (2024)
- [ChronoLLM: Customizing Language Models for Physics-Based Simulation Code Generation](https://mlsanity.com/paper/2508.13975/) (2025)
- [OMPILOT: Harnessing Transformer Models for Auto Parallelization to Shared Memory Computing Paradigms](https://mlsanity.com/paper/2511.03866/) (2025)
- [ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation](https://mlsanity.com/paper/2601.04327/) (2026)

## Abstract (arXiv)

The imperative need to scale computation across numerous nodes highlights the significance of efficient parallel computing, particularly in the realm of Message Passing Interface (MPI) integration. The challenging parallel programming task of generating MPI-based parallel programs has remained unexplored. This study first investigates the performance of state-of-the-art language models in generating MPI-based parallel programs. Findings reveal that widely used models such as GPT-3.5 and PolyCoder (specialized multi-lingual code models) exhibit notable performance degradation, when generating MPI-based programs compared to general-purpose programs. In contrast, domain-specific models such as MonoCoder, which are pretrained on MPI-related programming languages of C and C++, outperform larger models. Subsequently, we introduce a dedicated downstream task of MPI-based program generation by fine-tuning MonoCoder on HPCorpusMPI. We call the resulting model as MPIrigen. We propose an innovative preprocessing for completion only after observing the whole code, thus enabling better completion with a wider context. Comparative analysis against GPT-3.5 zero-shot performance, using a novel HPC-oriented evaluation method, demonstrates that MPIrigen excels in generating accurate MPI functions up to 0.8 accuracy in location and function predictions, and with more than 0.9 accuracy for argument predictions. The success of this tailored solution underscores the importance of domain-specific fine-tuning in optimizing language models for parallel computing code generation, paving the way for a new generation of automatic parallelization tools. The sources of this work are available at our GitHub MPIrigen repository: https://github.com/Scientific-Computing-Lab-NRCN/MPI-rigen