SuperCode: Sustainability PER AI-driven CO-DEsign
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917866395140096 |
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| author | Broekema, P. Chris van Nieuwpoort, Rob V. |
| author_facet | Broekema, P. Chris van Nieuwpoort, Rob V. |
| contents | Currently, data-intensive scientific applications require vast amounts of compute resources to deliver world-leading science. The climate emergency has made it clear that unlimited use of resources (e.g., energy) for scientific discovery is no longer acceptable. Future computing hardware promises to be much more energy efficient, but without better optimized software this cannot reach its full potential. In this vision paper, we propose a generic AI-driven co-design methodology, using specialized Large Language Models (like ChatGPT), to effectively generate efficient code for emerging computing hardware. We describe how we will validate our methodology with two radio astronomy applications, with sustainability as the key performance indicator. This paper is a modified version of our accepted SuperCode project proposal. We present it here in this form to introduce the vision behind this project and to disseminate the work in the spirit of Open Science and transparency. An additional aim is to collect feedback, invite potential collaboration partners and use-cases to join the project. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_08490 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SuperCode: Sustainability PER AI-driven CO-DEsign Broekema, P. Chris van Nieuwpoort, Rob V. Instrumentation and Methods for Astrophysics Artificial Intelligence Currently, data-intensive scientific applications require vast amounts of compute resources to deliver world-leading science. The climate emergency has made it clear that unlimited use of resources (e.g., energy) for scientific discovery is no longer acceptable. Future computing hardware promises to be much more energy efficient, but without better optimized software this cannot reach its full potential. In this vision paper, we propose a generic AI-driven co-design methodology, using specialized Large Language Models (like ChatGPT), to effectively generate efficient code for emerging computing hardware. We describe how we will validate our methodology with two radio astronomy applications, with sustainability as the key performance indicator. This paper is a modified version of our accepted SuperCode project proposal. We present it here in this form to introduce the vision behind this project and to disseminate the work in the spirit of Open Science and transparency. An additional aim is to collect feedback, invite potential collaboration partners and use-cases to join the project. |
| title | SuperCode: Sustainability PER AI-driven CO-DEsign |
| topic | Instrumentation and Methods for Astrophysics Artificial Intelligence |
| url | https://arxiv.org/abs/2412.08490 |