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

从大量数据中分析和学习的能力在人类努力的许多领域变得越来越重要，包括医学、科学和工程。高分辨率图像（例如医学成像、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换，以包含此类应用程序的存储、处理和分析需求，但理解系统堆栈的不同层如何相互交互并对端到端应用程序性能做出贡献是一项挑战。这个规划项目设想了雅典卫城的框架来应对这些挑战。ACROPOLIS将使系统软件的全面研究议程能够促进分析工作流程的快速灵活构建及其可扩展的执行。通过促进应用程序驱动程序的快速原型化，ACROPOLIS还可以使重要的科学发现有可能改善人类健康并更好地了解我们周围的世界。雅典卫城的研究也将教育许多学生，包括那些来自弱势群体的学生，他们将成为训练有素的劳动力的一部分，能够在未来很长一段时间内满足我们国家的需求。就更广泛的影响而言，雅典卫城将提供一个独特的研究和培训基础设施，这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（Ohio State University）已经建立的一些项目，如路易斯·斯托克斯少数族裔参与联盟（LSAMP），以及数据分析的新项目，将有助于招募研究生和本科生参与这一研究议程。该项目与美国国家科学基金会十大创意中的两个一致：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)极端规模下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)机器学习和分析的近内存架构；5)跨层可观察性和自省。具体来说，重点是设计一个端到端框架，灌输高性能，下一代，异构，可重构的硬件和软件堆栈，以促进实时交互，分析和机器学习，用于一系列科学学科，包括计算病理学和计算流体动力学和应急响应。该奖项反映了美国国家科学基金会的法定使命，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。

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.