Summary of The Landscape and Challenges Of Hpc Research and Llms, by Le Chen et al.
The Landscape and Challenges of HPC Research and LLMs
by 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
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the potential of large language models (LLMs) and encoder-decoder models to revolutionize deep learning in high-performance computing (HPC). Building on the success of LLMs in natural language processing and code-based tasks, the authors argue that adapting these techniques for HPC would be highly beneficial. The study presents their reasoning behind this position and highlights how existing ideas can be improved and adapted for HPC tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about using special kinds of computer models called large language models to make high-performance computing better. These models are good at understanding language and code, and the authors think they could also help with big computing jobs. The study explains why this is a good idea and shows how we can use these models in new ways. |
Keywords
* Artificial intelligence * Deep learning * Encoder decoder * Natural language processing
