用AI生成节能代码，助力科学计算可持续发展
SuperCode: Sustainability PER AI-driven CO-DEsign
- 用大语言模型自动优化代码以适配新型硬件
- 以射电天文应用验证，能效为关键指标
- 适合关注绿色计算与开源协作的研究者
当前数据密集型科学应用需大量计算资源来实现顶尖科研成果。气候危机表明，无限制使用资源（如能源）进行科学发现已不可接受。未来计算硬件虽更节能，但若软件未优化，其潜力无法发挥。本文提出一种通用的AI驱动协同设计方法，利用专用大语言模型（如ChatGPT）生成适配新兴硬件的高效代码。我们计划通过两个射电天文应用验证该方法，将可持续性作为核心性能指标。本文为已获采纳的SuperCode项目提案修改版，旨在阐述项目愿景，并以开放科学精神传播工作，同时征集反馈、合作方及应用场景。
原文摘要 · Abstract (English)
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.
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