MRI: Acquisition of a GPU Accelerated Vermont Advanced Computing Core

MRI：购买 GPU 加速的 Vermont 高级计算核心

基本信息

批准号：
1827314
负责人：
Adrian Delmaestro
金额：
$ 89.31万
依托单位：
University of Vermont & State Agricultural College
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2020-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1827314&HistoricalAwards=false
关键词：
MRI Acquisition GPU Accelerated Vermont

项目摘要

This project will enable interdisciplinary science through the acquisition of a high-performance computer cluster, named DeepGreen. Based on cutting-edge massively parallel graphics processing unit (GPU) technologies, DeepGreen will be utilized by the over 300 users from six Colleges at the University of Vermont, and throughout the Northeast. The unique hybrid architecture was designed to optimize artificial intelligence (AI) applications and will allow for rapid progress on problems of great societal importance. They include: quantum computing, drug discovery and design, safe robotics, control of adaptive crop pests, and new computer vision tools for use in the health care and transportation industries. As an example, DeepGreen will allow the training of neural networks on the world's largest brain imaging datasets of illicit drug users, yielding novel health and policy strategies to combat the opioid epidemic. A focus of the scientific and technical team is to broaden the number of personnel able to exploit GPU hardware for problem solving, producing the highly trained and diverse technical workforce required for the current and future AI economy. DeepGreen was designed by a team of experts from the physical, medical, biological, computational, and agricultural sciences, partnered with an experienced group of information technology professionals. It will be capable of over 8 petaflops of mixed precision calculations based on the latest NVIDIA Tesla V100 architecture with a hybrid design allowing high bandwidth message passing across heterogeneous compute nodes. Its extreme parallelism will facilitate research in three interconnected areas: quantum many-body systems, molecular simulation and modeling, and deep learning, artificial intelligence and evolutionary algorithms. DeepGreen will forge transformative research pipelines. It will enable the study of thousands of quantum entangled atoms, and millions of interacting components in biological systems providing insights into structure-function mechanisms. Machine learning and deep neural networks will exploit DeepGreen's Tensor Cores to solve diverse problems. These problems include: the development of coarse grained potentials for use in molecular dynamics simulations, real time dynamic processing of crowd sourced decision making for robotics, genomic sequencing of invasive pests, and feature recognition in medical imaging to distinguish cancerous tumors from benign nodules. Software designed for use on DeepGreen will be released to the public as open source, with other scientists and researchers being able to immediately use and extend it. This project will also support the next generation of data scientists. Training workshops focused on GPU computing and machine learning frameworks, new university courses, and partnerships with existing local NSF-funded graduate training initiatives, will drive broad utilization of DeepGreen.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.

该项目将通过收购一个名为 DeepGreen 的高性能计算机集群来实现跨学科科学。基于尖端的大规模并行图形处理单元 (GPU) 技术，DeepGreen 将被来自佛蒙特大学六所学院以及整个东北地区的 300 多名用户使用。独特的混合架构旨在优化人工智能（AI）应用，并将在具有重大社会意义的问题上取得快速进展。它们包括：量子计算、药物发现和设计、安全机器人技术、适应性作物害虫控制以及用于医疗保健和运输行业的新型计算机视觉工具。例如，DeepGreen 将允许在世界上最大的非法药物使用者的大脑成像数据集上训练神经网络，从而产生新的健康和政策策略来对抗阿片类药物的流行。科学和技术团队的重点是扩大能够利用 GPU 硬件解决问题的人员数量，培养当前和未来人工智能经济所需的训练有素且多样化的技术劳动力。 DeepGreen 是由来自物理、医学、生物、计算和农业科学的专家团队与经验丰富的信息技术专业人员团队合作设计的。它将能够基于最新的 NVIDIA Tesla V100 架构进行超过 8 petaflops 的混合精度计算，并采用混合设计，允许跨异构计算节点传递高带宽消息。其极端并行性将促进三个相互关联领域的研究：量子多体系统、分子模拟和建模以及深度学习、人工智能和进化算法。 DeepGreen 将打造变革性的研究渠道。它将能够研究生物系统中数千个量子纠缠原子和数百万个相互作用的成分，从而提供对结构功能机制的见解。机器学习和深度神经网络将利用 DeepGreen 的张量核心来解决各种问题。这些问题包括：用于分子动力学模拟的粗粒度潜力的开发、机器人众包决策的实时动态处理、入侵性害虫的基因组测序以及医学成像中区分癌性肿瘤和良性结节的特征识别。设计用于 DeepGreen 的软件将以开源形式向公众发布，其他科学家和研究人员可以立即使用和扩展它。该项目还将支持下一代数据科学家。专注于 GPU 计算和机器学习框架的培训研讨会、新的大学课程以及与现有的当地 NSF 资助的研究生培训计划的合作，将推动 DeepGreen 的广泛利用。该奖项反映了 NSF 的法定使命，并通过使用评估结果被认为值得支持。基金会的智力价值和更广泛的影响审查标准。