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: 2028942
• 负责人: Ponnuswamy Sadayappan
• 金额: $ 4.54万
• 依托单位: University of Utah
• 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 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028942&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.

分析大量数据并从中学习的能力在包括医学、科学和工程在内的许多人类工作领域中变得越来越重要。高分辨率图像的分析工作流(例如医学成像、天文测量)、科学模拟以及图形分析和机器学习的分析工作流通常非常耗时，因为涉及的数据规模极大。虽然现代数据中心的硬件元素正在经历快速转型，以适应此类应用程序的存储、处理和需求分析，但理解系统堆栈的不同层如何相互作用并对端到端应用程序性能做出贡献是具有挑战性的。该规划项目设想了雅典卫城框架来应对这些挑战。雅典卫城将支持关于系统软件的全面研究议程，以促进快速灵活地构建分析工作流程及其可扩展的执行。通过促进应用驱动程序的快速原型制作，雅典卫城还可以实现重要的科学发现，潜在地改善人类健康，并更好地了解我们周围的世界。雅典卫城的这项研究还将教育许多学生，包括那些来自代表不足的群体的学生，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。在更广泛的影响方面，雅典卫城将提供独特的研究和培训基础设施，将促进多学科的研究，并促进跨学科的融合研究。俄亥俄州立大学成熟的计划，如路易斯·斯托克斯少数群体参与联盟(LSAMP)以及数据分析方面的新计划，将有助于招募参与这一研究议程的研究生和本科生。该项目与NSF十大想法中的两个保持一致：利用数据革命和不断增长的融合研究，以及美国人工智能倡议。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)极端规模的数据移动复杂性建模，3)模式驱动的可伸缩通信和I/O系统，4)用于机器学习和分析的近内存体系结构，以及5)跨层可观察性和自省。具体地说，重点是设计端到端框架，灌输高性能、下一代、异类、可重新配置的硬件和软件堆栈，以促进包括计算病理学、计算流体动力学和应急响应在内的一系列科学学科的实时交互、分析和机器学习。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

Efficient Tiled Sparse Matrix Multiplication through Matrix Signatures

• DOI: 10.1109/sc41405.2020.00091
• 发表时间: 2020-11
• 期刊: SC20: International Conference for High Performance Computing, Networking, Storage and Analysis
• 作者: Süreyya Emre Kurt;Aravind Sukumaran-Rajam;F. Rastello;P. Sadayappan