MRI: RADiCAL: Reconfigurable Major Research Cyberinfrastructure for Advanced Computational Data Analytics and Machine Learning

MRI：RADiCAL：用于高级计算数据分析和机器学习的可重构主要研究网络基础设施

• 批准号: 2018627
• 负责人: Dhabaleswar Panda
• 金额: $ 77万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2018627&HistoricalAwards=false
• 关键词: MRI; RADiCAL; Reconfigurable; Major; Research

The analysis of high-resolution images in both two and three-dimensions is becoming important for many scientific areas, such as in medicine, astronomy and engineering. Discoveries in these disciplines often require analyzing millions of images. The analysis of these images is complex and requires many steps on powerful computers. Some of these steps require looking through lots of images while some of these steps require deep analysis of each image. In many cases, these analyses have to be completed quickly, i.e. in "real-time", so that information and insights can be provided to humans as they do their work. These kinds of operations require powerful computers consisting of many different, heterogeneous but simple computing components. These components need to be configured and reconfigured so that they can efficiently work together to do these large-scale analyses. In addition, the software that controls these computers also has to be intelligently designed so that these analyses can be run on the right types of configurations. This project aims to acquire the necessary computing components and assemble such a powerful computer (named RADiCAL). Research done using RADiCAL will result in important scientific discoveries that will make us more prosperous, improve our health, and enable us to better understand the world and universe around us. Doing this research will also educate many students, including those from under-represented groups, who will become part of a highly-trained workforce capable of addressing our nation's needs long into the future.The intellectual merit of RADiCAL is in the design a novel, high-performance, next-generation, heterogeneous, reconfigurable hardware and software stack to provide real-time interaction, analytics, machine/deep learning (ML/DL) and computing support for disciplines that involve massive observational and/or simulation data. RADiCAL will be built from commodity hardware, and designed for reconfiguration and observability. RADiCAL will enable a comprehensive research agenda on software that will facilitate rapid and flexible construction of analytics workflows and their scalable execution. Specific software research include: 1) a library with support for storage and retrieval of multi-resolution, multi-dimensional datasets, 2) scalable learning and inference modules, 3) data analytics middleware systems, and 4) context-sensitive human-in-the-loop ML models and libraries that encode domain expertise, coupling tightly with both lower level layers and the hardware components to facilitate scalable analysis and explainability. With the proposed hardware acquisition and software research, the transformative goal will be to facilitate decision-making and discovery in Computational Fluid Dynamics (CFD) and medicine (pathology). With respect to broader impacts, RADiCAL will provide a unique research, testing, and training infrastructure that will catalyze research in multiple disciplines as well as facilitate convergent research across disciplines. The advanced imaging applications and techniques for expert-assisted image analysis will be broadly applicable to other human-in-the-loop systems and have the potential to advance medicine and health. Projects that use RADiCAL will also provide unique test-beds for valuable empirical research on human-computer interaction and software engineering best practices. Well-established initiatives at The Ohio State University will facilitate the recruitment of graduate and undergraduate students from underrepresented groups for involvement in using the cyberinfrastructure. The heterogeneous and reconfigurable research instrument will be utilized to create sophisticated educational modules on how to co-design computational science experiments from the science goals to the underlying cyberinfrastructure. Tutorials and workshops will be organized at PEARC, Supercomputing and other conferences to share the research results and experience with the community.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.

在医学、天文学和工程学等许多科学领域，二维和三维高分辨率图像的分析变得越来越重要。这些学科的发现通常需要分析数百万张图像。这些图像的分析是复杂的，需要在强大的计算机上进行许多步骤。其中一些步骤需要查看大量图像，而其中一些步骤需要对每个图像进行深入分析。在许多情况下，这些分析必须快速完成，即“实时”完成，以便在人们工作时向他们提供信息和见解。这些操作需要由许多不同的、异构的但简单的计算组件组成的强大的计算机。需要对这些组件进行配置和重新配置，以便它们能够有效地协同工作来进行这些大规模分析。此外，控制这些计算机的软件也必须经过智能设计，以便这些分析可以在正确的配置类型上运行。该项目旨在获得必要的计算组件，并组装这样一台强大的计算机（名为RADiCAL）。使用RADiCAL进行的研究将带来重要的科学发现，使我们更加繁荣，改善我们的健康，并使我们能够更好地了解我们周围的世界和宇宙。这项研究还将教育许多学生，包括那些来自代表性不足群体的学生，他们将成为训练有素的劳动力的一部分，能够满足我们国家在未来很长一段时间的需求。RADiCAL的智力价值在于设计一种新颖的，高性能的，下一代的，异构的，可重新配置的硬件和软件堆栈，以提供实时交互，分析，机器/深度学习（ML/DL）和计算支持涉及大量观测和/或模拟数据的学科。RADiCAL将从商用硬件中构建，并设计用于重新配置和可观测性。RADiCAL将实现对软件的全面研究议程，这将有助于快速灵活地构建分析工作流程及其可扩展的执行。具体的软件研究包括：1）支持存储和检索多分辨率、多维数据集的库，2）可扩展的学习和推理模块，3）数据分析中间件系统，以及4）上下文敏感的人在回路ML模型和库，这些模型和库对领域专业知识进行编码，与低层和硬件组件紧密耦合，以促进可扩展的分析和可解释性。通过拟议的硬件收购和软件研究，变革目标将是促进计算流体动力学（CFD）和医学（病理学）的决策和发现。在更广泛的影响方面，RADiCAL将提供独特的研究、测试和培训基础设施，促进多学科的研究，并促进跨学科的融合研究。专家辅助图像分析的先进成像应用和技术将广泛适用于其他人在环系统，并有可能促进医学和健康。使用RADiCAL的项目还将为人机交互和软件工程最佳实践的宝贵实证研究提供独特的测试平台。 俄亥俄州州立大学的成熟举措将促进从代表性不足的群体中招募研究生和本科生参与使用网络基础设施。异构和可重构的研究工具将用于创建复杂的教育模块，介绍如何从科学目标到底层网络基础设施共同设计计算科学实验。该奖项反映了NSF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

LocationTrails: a federated approach to learning location embeddings

• DOI: 10.1145/3487351.3490964
• 发表时间: 2021-11
• 期刊: Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
• 作者: Saket Gurukar;Srinivas Parthasarathy;R. Ramnath;Catherine Calder;Sobhan Moosavi

Efficient Personalized and Non-Personalized Alltoall Communication for Modern Multi-HCA GPU-Based Clusters

• DOI: 10.1109/hipc56025.2022.00025
• 发表时间: 2022-12
• 期刊: 2022 IEEE 29th International Conference on High Performance Computing, Data, and Analytics (HiPC)
• 作者: K. Suresh;Akshay Paniraja Guptha;Benjamin Michalowicz;B. Ramesh;M. Abduljabbar;A. Shafi;H. Subramoni;D. Panda

Designing Hierarchical Multi-HCA Aware Allgather in MPI

• DOI: 10.1145/3547276.3548524
• 发表时间: 2022-08
• 期刊: Workshop Proceedings of the 51st International Conference on Parallel Processing
• 作者: Tu Tran;Benjamin Michalowicz;B. Ramesh;H. Subramoni;A. Shafi;D. Panda

Battle of the BlueFields: An In-Depth Comparison of the BlueField-2 and BlueField-3 SmartNICs

BlueFields 之战：BlueField-2 和 BlueField-3 SmartNIC 的深入比较

• 发表时间: 2023
• 期刊: 30th IEEE Hot Interconnects Symposium
• 作者: B. Michalowicz, K. Suresh

TEESec: Pre-Silicon Vulnerability Discovery for Trusted Execution Environments

• DOI: 10.1145/3579371.3589070
• 发表时间: 2023-06
• 期刊: Proceedings of the 50th Annual International Symposium on Computer Architecture
• 作者: Moein Ghaniyoun;Kristin Barber;Yuan Xiao;Yinqian Zhang;R. Teodorescu