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.

在许多人类努力中，包括医学，科学和工程学在内的许多步行中，分析和学习的能力变得越来越重要。分析高分辨率图像（例如医学成像，天空调查），科学模拟以及图形分析和机器学习的分析工作流程通常很耗时，因为涉及的数据范围极高。尽管现代数据中心的硬件元素正在经历快速转换，以包含对此类应用程序需求的存储，处理和分析 - 了解系统的不同层如何相互交互，并为端到端的应用程序性能做出挑战。雅典卫城将启用有关系统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。通过支持应用程序驱动力的快速原型制作，雅典卫城也可以使重要的科学发现有潜在地改善人类健康，并更好地了解我们周围的世界。由雅典卫城实施的研究还将教育许多学生，包括来自代表性不足的群体的学生，他们将成为一支受过训练的劳动力的一部分，能够在未来长期以来满足我们国家的需求。关于更广泛的影响，雅典卫城将提供独特的研究和培训基础设施，将促进多个学科的研究以及跨学科的方便收敛研究。俄亥俄州立大学的倡议良好，例如路易斯·斯托克斯（Louis Stokes）少数群体参与（LSAMP）以及数据分析的新计划，将有助于招募研究生和本科生，以参与这项研究议程。 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)跨层观察和内省。具体而言，重点是设计端到端框架的设计，该框架灌输了高性能，下一代，异构，可重构的硬件和软件堆栈，以促进实时互动，分析和机器学习，包括一系列科学学科，包括计算病理学和计算流动性动态动力学和紧急响应。该奖项反映了NSF的法定使命，并通过使用基金会的知识分子优点和更广泛的审查标准评估来诚实地获得支持。

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