mlsanityData to 7 Oct 2026

AutoParLLM: GNN-guided Context Generation for Zero-Shot Code Parallelization using LLMs

arXiv 2310.04047 ↗First posted 6 Oct 2023cs.LGcited by 5

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Abstract (arXiv)

In-Context Learning (ICL) has been shown to be a powerful technique to augment the capabilities of LLMs for a diverse range of tasks. This work proposes ourtool, a novel way to generate context using guidance from graph neural networks (GNNs) to generate efficient parallel codes. We evaluate ourtool xspace on 12 applications from two well-known benchmark suites of parallel codes: NAS Parallel Benchmark and Rodinia Benchmark. Our results show that ourtool xspace improves the state-of-the-art LLMs (e.g., GPT-4) by 19.9% in NAS and 6.48% in Rodinia benchmark in terms of CodeBERTScore for the task of parallel code generation. Moreover, ourtool xspace improves the ability of the most powerful LLM to date, GPT-4, by achieving ≈17% (on NAS benchmark) and ≈16% (on Rodinia benchmark) better speedup. In addition, we propose ourscore xspace for evaluating the quality of the parallel code and show its effectiveness in evaluating parallel codes. ourtool xspace is available at https://github.com/quazirafi/AutoParLLM.git.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 20 of its 54 references that are in the map. First posted on arXiv 6 Oct 2023.

Builds on

20 of 54 references.

References · 20

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  4. 2023Code Llama: Open Foundation Models for Codecited by 2,105
  5. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  6. 2022CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesiscited by 876
  7. 2020CodeBLEU: a Method for Automatic Evaluation of Code Synthesiscited by 468
  8. 2021MetaICL: Learning to Learn In Contextcited by 450
  9. 2022Complexity-Based Prompting for Multi-Step Reasoningcited by 387
  10. 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 271
  11. 2019Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networkscited by 241
  12. 2022OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalizationcited by 235
8 more
  1. 2023Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learningcited by 149
  2. 2023Finding Support Examples for In-Context Learningcited by 104
  3. 2023CodeBERTScore: Evaluating Code Generation with Pretrained Models of Codecited by 101
  4. 2023Symbol tuning improves in-context learning in language modelscited by 72
  5. 2023Pre-Training to Learn in Contextcited by 41
  6. 2023Learning to Parallelize with OpenMP by Augmented Heterogeneous AST Representationcited by 14
  7. 2021Automap: Towards Ergonomic Automated Parallelism for ML Modelscited by 12
  8. 2022Learning Intermediate Representations using Graph Neural Networks for NUMA and Prefetchers Optimizationcited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translationcited by 3
  2. 2025ACCeLLiuM: Supervised Fine-Tuning for Automated OpenACC Pragma Generationcited by 0
  3. 2026From Prompts to Performance: Evaluating LLMs for Task-based Parallel Code Generationcited by 0
  4. 2026ParEVO: Synthesizing Code for Irregular Data: High-Performance Parallelism through Agentic Evolutioncited by 0
  5. 2026POTracker: Optimizing Large Language Models for Standard-Compliant Power Outage Report Generationcited by 0