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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.02018 |
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| _version_ | 1866929235709394944 |
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| author | Chen, Le Ahmed, Nesreen K. Dutta, Akash Bhattacharjee, Arijit Yu, Sixing Mahmud, Quazi Ishtiaque Abebe, Waqwoya Phan, Hung Sarkar, Aishwarya Butler, Branden Hasabnis, Niranjan Oren, Gal Vo, Vy A. Munoz, Juan Pablo Willke, Theodore L. Mattson, Tim Jannesari, Ali |
| author_facet | Chen, Le Ahmed, Nesreen K. Dutta, Akash Bhattacharjee, Arijit Yu, Sixing Mahmud, Quazi Ishtiaque Abebe, Waqwoya Phan, Hung Sarkar, Aishwarya Butler, Branden Hasabnis, Niranjan Oren, Gal Vo, Vy A. Munoz, Juan Pablo Willke, Theodore L. Mattson, Tim Jannesari, Ali |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02018 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Landscape and Challenges of HPC Research and LLMs Chen, Le Ahmed, Nesreen K. Dutta, Akash Bhattacharjee, Arijit Yu, Sixing Mahmud, Quazi Ishtiaque Abebe, Waqwoya Phan, Hung Sarkar, Aishwarya Butler, Branden Hasabnis, Niranjan Oren, Gal Vo, Vy A. Munoz, Juan Pablo Willke, Theodore L. Mattson, Tim Jannesari, Ali Machine Learning 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. |
| title | The Landscape and Challenges of HPC Research and LLMs |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.02018 |