MRI: Acquisition of a Heterogeneous GPU Cluster to Facilitate Deep Learning Research at UMBC

MRI：收购异构 GPU 集群以促进 UMBC 的深度学习研究

• 批准号: 1920079
• 负责人: Hamed Pirsiavash
• 金额: $ 30万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1920079&HistoricalAwards=false
• 关键词: MRI; Acquisition; Heterogeneous; GPU; Cluster

This project acquires an instrument to pursue large-scale, data-intensive, end-to-end research, aiming to service several fields that include natural language processing (NLP), robotics, computer vision (CV), computer graphics, cybersecurity, medical analysis, and other heavily statistical areas. Utilizing very large data sets and performing intensive computation using Graphics Processing Units (GPU), the work focuses on vastly expanding the GPU computation power, storage, and access to data. This effort aims to reflect the discipline-wide shift within engineering towards model and system-building that require GPU computation and a continued focus within some computer and information science and engineering (CISE) disciplines towards deep learning and big data in need of big storage arrays, high memory servers, and horizontal scaling capabilities. The instrument contributes in preparing students with the skill to use clusters and other tools for handling large problems with the help of the cluster.Researchers will be able to tackle problems in various areas with the heterogeneous architecture of the cluster, since cluster-based computing can increase availability, reliability, and scalability. Moreover, performance on tasks that can be parallelized might be improved. Deep learning techniques will likely improve performance in predictive modeling. In turn, the cluster facilitates research across multiple discipline, it enables work in robotics, healthcare, medicine, as well as trust and fairness in machine learning. The proposal supports young faculty, women, and underrepresented minority groups such as UMBC (University of Maryland-Baltimore County), Meyerhoffer, and CWIT (Center of Women in Technology).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.

该项目获得了一种用于大规模、数据密集型、端到端研究的仪器，旨在服务于自然语言处理（NLP）、机器人、计算机视觉（CV）、计算机图形学、网络安全、医学分析和其他大量统计领域等多个领域。利用非常大的数据集并使用图形处理单元（GPU）执行密集计算，工作重点是大大扩展GPU的计算能力、存储和数据访问。这一努力旨在反映工程学学科范围内的转变，即转向需要GPU计算的模型和系统构建，以及一些计算机和信息科学与工程（CISE）学科对需要大存储阵列、高内存服务器和水平扩展能力的深度学习和大数据的持续关注。该乐器有助于培养学生使用集群和其他工具的技能，以便在集群的帮助下处理大型问题。由于基于集群的计算可以提高可用性、可靠性和可伸缩性，研究人员将能够利用集群的异构体系结构解决各种领域的问题。此外，可以并行化的任务的性能可能会得到改善。深度学习技术可能会提高预测建模的性能。反过来，该集群促进了跨多个学科的研究，它使机器人、医疗保健、医学以及机器学习中的信任和公平成为可能。该提案支持年轻教师、女性和未被充分代表的少数群体，如马里兰大学巴尔的摩分校（University of Maryland-Baltimore County）、迈耶霍夫大学（Meyerhoffer）和CWIT （women in Technology Center）。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。

项目成果

Unsupervised Radio Scene Analysis Using Neural Expectation Maximization

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• 发表时间: 2022
• 期刊: 2022.
• 作者: Hao Chen;Seung-Jun Kim
• 通讯作者: Seung-Jun Kim

Jointly Identifying and Fixing Inconsistent Readings from Information Extraction Systems

联合识别和修复信息提取系统的不一致读数

• 发表时间: 2022
• 期刊: Third Deep Learning Inside Out (DeeLIO
• 作者: Padia, Ankur;Ferraro, Francis;Finin, Tim
• 通讯作者: Finin, Tim

Knowledge-Embedded Narrative Construction from Open Source Intelligence

来自开源情报的知识嵌入叙事构建

• 发表时间: 2023
• 期刊: Thirty-Seventh AAAI Conference on Artificial Intelligence Doctoral Consortium; AAAI
• 作者: Priyanka Ranade

SWeeT: Security Protocol for Wearables Embedded Devices’ Data Transmission

SWeET：可穿戴设备嵌入式设备数据传输的安全协议

• DOI: 10.1109/healthcom54947.2022.9982744
• 发表时间: 2022
• 期刊: Application & Services (HealthCom
• 作者: Ebrahimabadi, Mohammad;Younis, Mohamed;Lalouani, Wassila;Alshaeri, Abdulaziz;Karimi, Naghmeh
• 通讯作者: Karimi, Naghmeh

Towards CNN-Based Registration of Craniocaudal and Mediolateral Oblique 2-D X-ray Mammographic Images

基于 CNN 的颅尾和内侧倾斜二维 X 射线乳房 X 射线图像配准

• 发表时间: 2019
• 期刊: 2019.
• 作者: William C. Walton;Seung-Jun Kim;Susan C. Harvey;Lisa A. Mullen;David W. Porter
• 通讯作者: David W. Porter