Collaborative Research: CNS Core: Medium: Terabyte-scale Tiered Memory Management

随着应用程序对存储器的需求以爆炸性的速度增长，我们目睹了当前占主导地位的计算机存储器技术（“DRAM”）在容量上按比例增加的速度放缓。这种不断增长的差距导致对大容量存储器系统的需求，这些系统要么在网络上分解DRAM存储器组件，要么采用提供比DRAM更高容量但性能更低的存储器技术。这种在单个计算节点内划分为不同性能层的更大存储器的趋势提出了当前存储器管理机制和技术无法跟上的挑战。关键挑战包括如何表征应用程序和工作负载的内存行为，以及如何、何时和在何处放置和迁移数据，同时降低性能、能源和资金开销。本研究项目将探索这些基本挑战，并开发和评估专门针对这些大规模分层内存系统的解决方案。为了最有效，这些解决方案将跨越计算机硬件（即，处理器和存储器模块）和系统软件（即，操作系统）。结合硬件和软件的研究方法，沿着持续的解决方案原型设计，确保了目标挑战是真实的，解决方案不仅对学术界，而且对行业和最终用户都有影响。这项研究是及时和必要的，因为一个全面的，低开销的，分层的内存管理系统是释放新兴内存技术的潜力的先决条件。反过来，这些技术是必要的，特别是对于云计算，以实现应用程序所需的性能水平，同时保持货币和环境成本低。首先，分层存储器的有效使用将减少系统中安装的存储器组件的数量，从而减少与它们相关的嵌入式和操作碳。其次，开发的管理技术将使单个计算节点能够成功地为更高的应用程序负载提供服务，减少计算和内存的碳足迹。其他社会效益包括独特的培训，这个硬件-软件研究项目将提供给学生，包括本科生和研究生。该项目还很有可能扩大对计算的参与。该项目的主要调查人员有为女学生提供咨询的记录;参与的大学致力于扩大参与;学生招聘环境受益于来自历史上在计算机技术方面代表性不足的群体的大量学生。德克萨斯大学奥斯汀分校是一所公认的西班牙裔服务大学。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

As application demand for memory increases at an explosive pace, we witness a slowdown in the rate at which the currently dominant computer memory technology ("DRAM") scales up in capacity. This growing gap leads to a need for large-capacity memory systems that either disaggregate the DRAM memory components across a network, or adopt a memory technology that offers higher capacity than DRAM but at slower performance. This trend toward larger memories that are split into different performance tiers within a single compute node poses challenges that current memory management mechanisms and techniques cannot keep up with. The critical challenges include how to characterize the memory behavior of applications and workloads and how, when, and where to place and migrate data with low performance, energy, and monetary overhead. This research project will explore these fundamental challenges and develop and evaluate solutions specifically for these large-scale, tiered memory systems. To be most effective, these solutions will span both the computer hardware (i.e., the processor and memory modules) and system software (i.e., the operating system). The combined hardware-software research approach, along with continuous prototyping of the proposed solutions, ensures that the challenges targeted are real and that the solutions will have impact not only on academia, but also on industry and end users. This research is timely and necessary because a comprehensive, low-overhead, tiered memory management system is a prerequisite for unleashing the potential of emerging memory technologies. These technologies are, in turn, necessary, especially for cloud computing, to achieve the performance levels needed for applications, while keeping costs, both monetary and environmental, low. First, the effective use of tiered memories will both reduce the number of memory components installed in systems, thus reducing the embedded and operational carbon associated with them. Second, the developed management techniques will enable a single computing node to successfully serve a higher application load, reducing the carbon footprint of compute as well as memory. Other societal benefits include the unique training this hardware-software research project will provide to students, including undergraduate and graduate students. The project also has a high likelihood of broadening participation in computing. The primary investigators on this project have a track record of advising female students; the participating universities are committed to broadening participation; and the student recruitment environment benefits from a large number of students from groups that are historically underrepresented in computer technology. The University of Texas at Austin is a recognized Hispanic-Serving University.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.