Authors: 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

Published on: February 03, 2024

Impact Score: 8.22

Arxiv code: Arxiv:2402.02018

Summary

• What is new: Applying language model-based techniques, particularly large language models, to high-performance computing tasks.
• Why this is important: The potential of language models hasn’t been fully harnessed for high-performance computing.
• What the research proposes: Adapting encoder-decoder models and prompt-based techniques for HPC tasks.
• Results: Highlights how these adaptations could improve efficiency and functionality in high-performance computing.

Technical Details

Technological frameworks used: Encoder-decoder models, prompt-based techniques

Models used: Large language models (LLMs)

Data used: Not specified

Potential Impact

Tech companies involved in high-performance computing, cloud services, and large-scale data processing could benefit or face disruption.

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