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Quazi Ishtiaque Mahmud

2 accepted papers

2025

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

NAACL 2025long

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 AutoParLLM, a novel way to generate context using guidance from graph neural networks (GNNs) to generate efficient parallel codes. We evaluate Auto…

2023

PERFOGRAPH: A Numerical Aware Program Graph Representation for Performance Optimization and Program Analysis

NeurIPS 2023poster

The remarkable growth and significant success of machine learning have expanded its applications into programming languages and program analysis. However, a key challenge in adopting the latest machine learning methods is the representation of programming languages which has a direct impact on the a…

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