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

日常生活中许多重要的计算机应用都依赖于对大量数据的处理。虽然将数据从存储设备移动到处理组件可能非常耗时，但随着核心数量的增加和新兴应用程序的出现，在计算机系统内移动数据也可能会导致显著的延迟，从而损害应用程序性能和能源效率。遗憾的是，将这种数据移动开销降至最低的现有解决方案潜力有限。因此，必须探索一种全面的方法来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接近数据计算(NDC)的模型，以有效地处理不同类别应用程序的数据处理需求。PIS的目标是将他们对NDC的研究与宾夕法尼亚州立大学的教育活动和学生培训结合起来，以培养未来的科学和工程劳动力。外展活动包括让本科生参与NDC研究，并与宾夕法尼亚州立大学的CSATS(科学与学校中心)和VIEW(工程周末访问)计划合作，参与正在进行的面向STEM的K-12活动。该项目旨在通过采用跨层方法来探索将计算更接近数据的潜在好处，从新的角度重新审视近数据计算的概念。因此，这个项目不是只考虑处理器核心的硬件与DRAM之间的布尔极端计算(就像过去的尝试那样)，而是探索这两者之间的丰富的可能性。具体地说，本研究着眼于来自三个重要应用领域(高性能计算、嵌入式/移动计算和数据中心计算)的新兴多核系统和多线程应用，试图解决深度存储层次环境中近数据计算的“在哪里”、“何时”、“什么”和“如何”的问题。这种将计算更贴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伸缩性的最大障碍。

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.