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.

我们日常生活中许多重要的计算机应用取决于处理大量数据。尽管将数据从存储设备转移到处理组件可能非常耗时，但随着核心计数和新兴应用程序的增加，计算机系统中的数据也会引起大量潜伏期，从而损害应用程序性能和能源效率。不幸的是，最小化此数据移动开销的现有解决方案的潜力有限。因此，探索一种全面方法来最大程度地减少数据移动，并从当今使用的以计算为中心的模型转变为以数据为中心或接近数据计算的模型（NDC）模型有效地处理不同类别应用程序的数据处理需求。 PI的目的是将他们的NDC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，以培育科学和工程学领域的未来劳动力。宣传活动包括吸引本科生参与NDC研究，并与CSAT（科学与学校中心）合作，并在宾夕法尼亚州立大学观看（访问工程周末）计划，以参与正在进行的型干k-k-12活动。本项目的目的是通过在探索探索探索的情况下进行近距离探索的近乎数据的概念，以重新审查近乎数据的计算概念。因此，该项目不仅考虑了处理器核心与DRAM的硬件（如过去尝试）中的近数据计算的布尔值，而是探索了这两个项目之间的丰富可能性。 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.这种将计算更接近数据的全面方法旨在打破记忆墙，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