XPS: FULL: A Fresh Look at Near Data Computing: Coordinated Data and Computation Government

XPS：完整：近数据计算的新视角：协调数据和计算政府

• 批准号: 1629129
• 负责人: Mahmut Kandemir
• 金额: $ 87.5万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1629129&HistoricalAwards=false
• 关键词: XPS; FULL; Fresh; Look; Near

Many important computer applications in our daily lives depend on processing large amounts of data. While moving data from storage devices to processing components can be very time consuming, with increasing core counts and emerging applications, moving data within a computer system can also incur significant latencies, thereby hurting application performance and energy efficiency. Unfortunately, existing solutions to minimize this data movement overhead have limited potential. Thus, it has become essential to explore a holistic approach for minimizing data movements, and shifting from the compute-centric model being used today to a data-centric or near-data computing (NDC) model for effectively handling the data processing needs of different classes of applications. The PIs aim to integrate their research on NDC with the educational activities and student training at Penn State for nurturing the future workforce in science and engineering. The outreach activities include engaging undergraduates in the NDC research, and working with the CSATS (Center for Science and the Schools) and VIEW (Visit In Engineering Weekend) programs at Penn State to get involved with the ongoing STEM-oriented K-12 activities.This project aims to revisit the near-data computing concept from a fresh perspective by undertaking a cross-layer approach for exploring the potential benefits of moving computation closer to data. Thus, instead of considering only the Boolean extremes of near-data computing in the hardware of processor core vs. the DRAM (as in the case of past attempts), this project explores a rich spectrum of possibilities between these two. Specifically, focusing on emerging multicore systems and multithreaded applications from three important application domains (high performance computing, embedded/mobile computing, and datacenter computing), this research tries to address the "where", "when", "what", and "how" questions of near-data computing in the context of deep memory hierarchies. This comprehensive approach to moving computation closer to the data aims to break the memory wall, which is the biggest barrier to the scalability of emerging chip multiprocessors.

我们日常生活中的许多重要计算机应用都依赖于处理大量数据。虽然将数据从存储设备移动到处理组件可能非常耗时，但随着核数的增加和新兴应用的出现，在计算机系统内移动数据也可能导致显著的延迟，从而损害应用性能和能源效率。不幸的是，现有的解决方案，以最大限度地减少这种数据移动开销的潜力有限。因此，必须探索一种整体方法，以最大限度地减少数据移动，并从目前使用的以计算为中心的模型转变为以数据为中心或近数据计算（NDC）模型，以有效地处理不同类别应用程序的数据处理需求。PI旨在将他们对NDC的研究与宾夕法尼亚州立大学的教育活动和学生培训相结合，以培养未来的科学和工程劳动力。外联活动包括让本科生参与国家数据中心的研究，与CSATS合作（科学和学校中心）和VIEW（Visit In Engineering Weekend）计划，参与正在进行的面向STEM的K-12活动。该项目旨在通过进行跨学科的研究，从一个新的角度重新审视近数据计算的概念。层的方法来探索移动计算更接近数据的潜在好处。因此，该项目不是只考虑处理器核心与DRAM硬件中的接近数据计算的布尔极端（如过去尝试的情况），而是探索这两者之间的丰富可能性。具体而言，集中在新兴的多核系统和多线程应用程序从三个重要的应用领域（高性能计算，嵌入式/移动的计算，和数据中心计算），本研究试图解决“在哪里”，“何时”，“什么”，和“如何”的问题，近数据计算的上下文中的深内存层次结构。这种将计算更接近数据的综合方法旨在打破内存墙，这是新兴芯片多处理器可扩展性的最大障碍。

项目成果

Getting more performance with polymorphism from emerging memory technologies

• DOI: 10.1145/3319647.3325826
• 发表时间: 2019-05
• 期刊: Proceedings of the 12th ACM International Conference on Systems and Storage
• 作者: Iyswarya Narayanan;Aishwarya Ganesan;Anirudh Badam;Sriram Govindan;Bikash Sharma;A. Sivasubramaniam