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.

对两维和三维的高分辨率图像的分析对于许多科学领域，例如在医学，天文学和工程学中都变得很重要。这些学科的发现通常需要分析数百万张图像。这些图像的分析很复杂，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，这些分析必须迅速完成，即“实时”，以便可以在人类完成工作时向人类提供信息和见解。这类操作需要功能强大的计算机，这些计算机由许多不同的，异质但简单的计算组件组成。这些组件需要配置和重新配置，以便它们可以有效地进行这些大规模分析。此外，控制这些计算机的软件还必须智能设计，以便可以在正确的配置类型上运行这些分析。该项目旨在获取必要的计算组件并组装如此强大的计算机（命名为激进）。使用激进的研究将导致重要的科学发现，这将使我们更加繁荣，改善健康，并使我们能够更好地了解周围的世界和宇宙。进行这项研究还将教育许多学生，包括来自代表性不足的团体的学生，他们将成为能够在未来长期以来满足我们国家需求的训练有素的劳动力中的一部分。激进的理智优点在设计中是一部小说，高性能，下一代，异质，可重构硬件和软件堆栈，以提供实时互动，分析，机器/深度学习（ML/DL）以及涉及大量观察和/或仿真数据的学科的计算支持。激进主义将由商品硬件建造，并设计用于重新配置和可观察性。 Radical将实现有关软件的全面研究议程，该议程将促进分析工作流程及其可扩展执行的快速，灵活构建。特定软件研究包括：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））编码域专业知识的环路ML模型和库，与较低级别的层和硬件组件紧密结合，以促进可扩展分析和解释性。通过拟议的硬件获取和软件研究，变革性的目标将是促进计算流体动力学（CFD）和医学（病理学）中的决策和发现。关于更广泛的影响，激进主义将提供独特的研究，测试和培训基础设施，该基础设施将催化多个学科的研究，并促进跨学科的收敛研究。高级成像应用和专家辅助图像分析的技术将广泛适用于其他人类的系统，并有可能进步医学和健康。使用激进的项目还将为人类计算机互动和软件工程最佳实践提供独特的测试床。 俄亥俄州立大学的公认倡议将有助于招募来自代表性不足的团体的研究生和本科生，以参与使用网络基础设施。将利用异质和可重构的研究工具来创建有关如何从科学目标到基础网络基础设施共同设计计算科学实验的复杂教育模块。教程和讲习班将在Pearc举行，超级计算和其他会议，以与社区分享研究结果和经验。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响来通过评估来支持的。

项目成果

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

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

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

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

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