Collaborative Research: PPoSS: Planning: A Cross-Layer Observable Approach to Extreme Scale Machine Learning and Analytics

协作研究：PPoSS：规划：超大规模机器学习和分析的跨层可观察方法

• 批准号: 2028944
• 负责人: Srinivasan Parthasarathy
• 金额: $ 20.45万
• 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 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028944&HistoricalAwards=false
• 关键词: Collaborative; Research; PPoSS; Planning; Cross

The ability to analyze and learn from large volumes of data is becoming important in many walks of human endeavor, including medicine, science, and engineering. Analysis workflows for high-resolution images (e.g. medical imaging, sky surveys), scientific simulations, as well as those for graph analytics and machine learning are typically time consuming because of the extreme scales of data involved. While the hardware elements of the modern data center are undergoing a rapid transformation to embrace the storage, processing, and analysis of needs of such applications - understanding of how the different layers of the systems stack interact with one another and contribute to end-to-end application performance is challenging. This planning project envisions the ACROPOLIS framework to address these challenges. ACROPOLIS will enable a comprehensive research agenda on systems software that will facilitate rapid and flexible construction of analytics workflows and their scalable execution. By facilitating the rapid prototyping of application drivers ACROPOLIS can also enable important scientific discoveries to potentially improve human health and better understand the world around us. The research enabled by ACROPOLIS 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. With respect to broader impacts, ACROPOLIS will provide a unique research and training infrastructure that will catalyze research in multiple disciplines as well as facilitate convergent research across disciplines. Well-established initiatives at The Ohio State University, such as the Louis Stokes Alliances for Minority Participation (LSAMP) as well as new programs in Data Analytics, will facilitate the recruitment of graduate and undergraduate students for involvement in this research agenda. This project is aligned with two of NSF’s 10 Big Ideas: Harnessing the Data Revolution and Growing Convergence Research, as well as the American AI Initiative.The project addresses five key research pillars: 1) Flexible abstractions for parallel computation and data representation, 2) Modeling data movement complexity at extreme scales, 3) Pattern-driven scalable communication and I/O systems, 4) Near-memory architectures for machine learning and analytics, and 5) Cross-layer observability and introspection. Specifically, the focus is on the design of an end-to-end framework inculcating a high-performance, next-generation, heterogeneous, reconfigurable hardware and software stack to facilitate real-time interaction, analytics, and machine learning for a range of scientific disciplines including Computational Pathology and Computational Fluid Dynamics and Emergency Response.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.

从大量数据中分析和学习的能力在人类奋进的许多行业中变得越来越重要，包括医学，科学和工程。高分辨率图像（例如医学成像、巡天）、科学模拟以及图形分析和机器学习的分析工作流程通常非常耗时，因为涉及的数据规模非常大。虽然现代数据中心的硬件元素正在经历快速转型，以满足此类应用程序的存储、处理和分析需求，但了解系统堆栈的不同层如何相互作用并有助于端到端应用程序性能是一项挑战。 该规划项目设想通过ACROPOLIS框架来应对这些挑战。ACROPOLIS将为系统软件提供全面的研究议程，以促进快速灵活地构建分析工作流程及其可扩展的执行。通过促进应用程序驱动程序的快速原型设计，ACROPOLIS还可以实现重要的科学发现，从而可能改善人类健康并更好地了解我们周围的世界。由ACROPOLIS启用的研究还将教育许多学生，包括那些来自代表性不足的群体，谁将成为一个训练有素的劳动力的一部分，能够解决我们国家的需求很长一段时间到未来。关于更广泛的影响，ACROPOLIS将提供独特的研究和培训基础设施，促进多学科的研究，并促进跨学科的融合研究。 在俄亥俄州州立大学，如路易斯·斯托克斯联盟少数民族参与（LSAMP）以及数据分析的新方案完善的举措，将促进招聘研究生和本科生参与这一研究议程。该项目与NSF的十大理念中的两个一致：利用数据革命和不断增长的融合研究，以及美国人工智能计划。该项目涉及五个关键研究支柱：1）并行计算和数据表示的灵活抽象，2）在极端规模下对数据移动复杂性建模，3）模式驱动的可扩展通信和I/O系统，4）用于机器学习和分析的近内存架构，以及5）跨层可观察性和内省。具体来说，重点是设计一个端到端的框架，灌输一个高性能，下一代，异构，可重新配置的硬件和软件堆栈，以促进实时交互，分析，该奖项反映了NSF的法定使命，并被认为是值得支持的，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。

项目成果

FairEGM: Fair Link Prediction and Recommendation via Emulated Graph Modification

• DOI: 10.1145/3551624.3555287
• 发表时间: 2022-01
• 期刊: Proceedings of the 2nd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization
• 作者: Sean Current;Yuntian He;Saket Gurukar;Srinivas Parthasarathy

WebMILE: Democratizing Network Representation Learning at Scale

• DOI: 10.14778/3554821.3554883
• 发表时间: 2022-08
• 期刊: Proc. VLDB Endow.
• 作者: Yuntian He;Yue Zhang-;Srinivas Parthasarathy

Using Undervolting as an on-Device Defense Against Adversarial Machine Learning Attacks

• DOI: 10.1109/host49136.2021.9702287
• 发表时间: 2021-07
• 期刊: 2021 IEEE International Symposium on Hardware Oriented Security and Trust (HOST)
• 作者: Saikat Majumdar;Mohammad Hossein Samavatian;Kristin Barber;R. Teodorescu