MRI: Acquisition of a high-performance computing instrument to support deep learning, modeling/simulation, and visualization for STEM

该项目将获得一个用于高性能计算的仪器，旨在支持深度学习、建模和可视化项目的计算需求。预计该工具将大大加快目前受现有计算资源限制的项目，并使目前无法用现有资源完成的项目能够在合理的时间内（如果有的话）完成。深度学习的训练时间可以预期几个数量级的改进，模拟和建模、数据挖掘以及可视化和演示的准备也是如此。深度学习模型和科学模拟过于庞大，无法在当前的校园计算资源上运行，而这台仪器将成为可能。作为促进竞争性研究的既定方案（EPSCoR）的一部分，预计该州将作出以下贡献：-科学进步-例如，在了解大规模环流模式对物理结构和生态栖息地的影响方面；在STEM项目中越来越多地使用深度学习；-通过新兴的可视化技术产生对数据和模型的新理解；-通过网络动态可视化向K-16教育和公众推广；-使用上述方法以及使用先进的计算工具，对科学、技术、工程和数学（STEM）学科的本科生和研究生进行培训；-在缅因州服务不足的农村社区增加对计算资源的获取。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project, acquiring an instrument for high performance computing, aims to support computational needs for deep learning, modeling, and visualization projects. The instrument is expected to drastically speed up projects that are now limited by available computational resources, as well as enable projects to be undertaken that currently cannot be done with existing resources in a reasonable amount of time, if at all. Deep learning training times can expect several orders of magnitude improvement, as can simulation and modeling, data mining, and preparation of visualizations and demonstrations. Deep learning models and scientific simulations that are too large to be run on current campus computing resources will be possible with the instrument.The following contributions are expected in this state, which is part of the Established Program to Stimulate Competitive Research (EPSCoR): - Scientific advances - for example, in understanding the influence of large-scale circulation patterns on physical structures and ecological habitats; the increased use of deep learning in STEM projects; - Emergence of new forms of understanding of data and models via emerging visualization techniques; - Outreach to K-16 education and the public through web-based dynamic visualizations; - Training of undergraduate and graduate students across science, technology, engineering, and mathematics (STEM) disciplines using the methodologies mentioned as well as the use of advanced computational tools; and - Enhanced access to computational resources in underserved rural communities throughout Maine.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.