The Landscape and Challenges of HPC Research and LLMs

• URL: http://arxiv.org/abs/2402.02018v3
• Date: Wed, 7 Feb 2024 01:51:21 GMT
• Title: The Landscape and Challenges of HPC Research and LLMs
• 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
• Abstract summary: Large language models (LLMs) have revolutionized the field of deep learning. encoder-decoder models and prompt-based techniques have shown immense potential for natural language processing and code-based tasks.
• Score: 12.57518012358534
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, language models (LMs), especially large language models (LLMs), have revolutionized the field of deep learning. Both encoder-decoder models and prompt-based techniques have shown immense potential for natural language processing and code-based tasks. Over the past several years, many research labs and institutions have invested heavily in high-performance computing, approaching or breaching exascale performance levels. In this paper, we posit that adapting and utilizing such language model-based techniques for tasks in high-performance computing (HPC) would be very beneficial. This study presents our reasoning behind the aforementioned position and highlights how existing ideas can be improved and adapted for HPC tasks.

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