Search Results for author:
Found 9 papers, 2 papers with code
HPC-GPT: Integrating Large Language Model for High-Performance Computing
Large Language Models (LLMs), including the LLaMA model, have exhibited their efficacy across various general-domain natural language processing (NLP) tasks.
Towards Zero Memory Footprint Spiking Neural Network Training
Our design is able to achieve a $\mathbf{58. 65\times}$ reduction in memory usage compared to the current SNN node.
Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought
Recent advancements in large-scale models, such as GPT-4, have showcased remarkable capabilities in addressing standard queries.
Data Race Detection Using Large Language Models
Large language models (LLMs) are demonstrating significant promise as an alternate strategy to facilitate analyses and optimizations of high-performance computing programs, circumventing the need for resource-intensive manual tool creation.
Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C++
In this study, we present a novel dataset for training machine learning models translating between OpenMP Fortran and C++ code.
LM4HPC: Towards Effective Language Model Application in High-Performance Computing
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.
Structured Thoughts Automaton: First Formalized Execution Model for Auto-Regressive Language Models
We introduce a low-level language to write "cognitive program" for this execution model.
Making Machine Learning Datasets and Models FAIR for HPC: A Methodology and Case Study
The FAIR Guiding Principles aim to improve the findability, accessibility, interoperability, and reusability of digital content by making them both human and machine actionable.
Finding Reusable Machine Learning Components to Build Programming Language Processing Pipelines
Programming Language Processing (PLP) using machine learning has made vast improvements in the past few years.
