The Landscape and Challenges of HPC Research and LLMs

no code implementations • 3 Feb 2024 • Le Chen, Nesreen K. Ahmed, Akash Dutta, Arijit Bhattacharjee, Sixing Yu, Quazi Ishtiaque Mahmud, Waqwoya Abebe, Hung Phan, Aishwarya Sarkar, Branden Butler, Niranjan Hasabnis, Gal Oren, Vy A. Vo, Juan Pablo Munoz, Theodore L. Willke, Tim Mattson, Ali Jannesari

Recently, language models (LMs), especially large language models (LLMs), have revolutionized the field of deep learning.

Language Modelling Position

AUTOPARLLM: GNN-Guided Automatic Code Parallelization using Large Language Models

no code implementations • 6 Oct 2023 • Quazi Ishtiaque Mahmud, Ali TehraniJamsaz, Hung D Phan, Nesreen K. Ahmed, Ali Jannesari

Parallelizing sequentially written programs is a challenging task.

Code Generation

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

1 code implementation • NeurIPS 2023 • Ali TehraniJamsaz, Quazi Ishtiaque Mahmud, Le Chen, Nesreen K. Ahmed, Ali Jannesari

The remarkable growth and significant success of machine learning have expanded its applications into programming languages and program analysis.

Learning to Parallelize with OpenMP by Augmented Heterogeneous AST Representation

no code implementations • 9 May 2023 • Le Chen, Quazi Ishtiaque Mahmud, Hung Phan, Nesreen K. Ahmed, Ali Jannesari

However, applying machine learning techniques to parallelism detection presents several challenges, such as the lack of an adequate dataset for training, an effective code representation with rich information, and a suitable machine learning model to learn the latent features of code for diverse analyses.

Program Synthesis