# From Prompts to Performance: Evaluating LLMs for Task-based Parallel Code Generation

arXiv: https://arxiv.org/abs/2602.22240 · HTML: https://mlsanity.com/paper/2602.22240/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 24 Feb 2026.

## Builds on (most cited first, 8 of 8)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [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)
- [AutoParLLM: GNN-guided Context Generation for Zero-Shot Code Parallelization using LLMs](https://mlsanity.com/paper/2310.04047/) (2023)
- [LLM Benchmarking with LLaMA2: Evaluating Code Development Performance Across Multiple Programming Languages](https://mlsanity.com/paper/2503.19217/) (2025)

## Abstract (arXiv)

Large Language Models (LLM) show strong abilities in code generation, but their skill in creating efficient parallel programs is less studied. This paper explores how LLMs generate task-based parallel code from three kinds of input prompts: natural language problem descriptions, sequential reference implementations, and parallel pseudo code. We focus on three programming frameworks: OpenMP Tasking, C++ standard parallelism, and the asynchronous many-task runtime HPX. Each framework offers different levels of abstraction and control for task execution. We evaluate LLM-generated solutions for correctness and scalability. Our results reveal both strengths and weaknesses of LLMs with regard to problem complexity and framework. Finally, we discuss what these findings mean for future LLM-assisted development in high-performance and scientific computing.