Papers by Quazi Ishtiaque Mahmud

1 papers
AutoParLLM: GNN-guided Context Generation for Zero-Shot Code Parallelization using LLMs (2025.naacl-long)

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Challenge: In-Context Learning (ICL) is a powerful technique to augment the capabilities of LLMs for a diverse range of tasks.
Approach: They propose a way to generate context using guidance from graph neural networks to generate efficient parallel codes.
Outcome: The proposed method improves state-of-the-art LLMs by 19.9% and 6.48% on NAS and rodinia benchmarks.

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