most citedHPC-GPT: Integrating Large Language Model for High-Performance Computing

53 citations · 112 across the 7 of their papers we have counts for

collaborators

7 papers

cs.DC2023★ 53 cited

HPC-GPT: Integrating Large Language Model for High-Performance Computing

Xianzhong Ding, Le Chen, Murali Emani +6

Large Language Models (LLMs), including the LLaMA model, have exhibited their efficacy across various general-domain natural language processing (NLP) tasks. However, their perform…

cs.LG2023★ 20 cited

Data Race Detection Using Large Language Models

Le Chen, Xianzhong Ding, Murali Emani +3

Large language models (LLMs) are demonstrating significant promise as an alternate strategy to facilitate analyses and optimizations of high-performance computing programs, circumv…

cs.LG2023★ 35 cited

LM4HPC: Towards Effective Language Model Application in High-Performance Computing

Le Chen, Pei-Hung Lin, Tristan Vanderbruggen +3

In recent years, language models (LMs), such as GPT-4, have been widely used in multiple domains, including natural language processing, visualization, and so on. However, applying…

cs.CL2023★ 1 cited

Structured Thoughts Automaton: First Formalized Execution Model for Auto-Regressive Language Models

Tristan Vanderbruggen, Chunhua Liao, Peter Pirkelbauer +1

In recent months, Language Models (LMs) have become a part of daily discourse, with focus on OpenAI and the potential of Artificial General Intelligence (AGI). Furthermore, the lea…

cs.DC2022★ 3 cited

Towards Seamless Management of AI Models in High-Performance Computing

Sixing Yu, Murali Emani, Chunhua Liao +4

With the increasing prevalence of artificial intelligence (AI) in diverse science/engineering communities, AI models emerge on an unprecedented scale among various domains. However…

cs.LG2022

Making Machine Learning Datasets and Models FAIR for HPC: A Methodology and Case Study

Pei-Hung Lin, Chunhua Liao, Winson Chen +3

The FAIR Guiding Principles aim to improve the findability, accessibility, interoperability, and reusability of digital content by making them both human and machine actionable. Ho…

cs.LG2022

Finding Reusable Machine Learning Components to Build Programming Language Processing Pipelines

Patrick Flynn, Tristan Vanderbruggen, Chunhua Liao +3

Programming Language Processing (PLP) using machine learning has made vast improvements in the past few years. Increasingly more people are interested in exploring this promising f…