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

随着应用程序对内存的需求以爆炸性的速度增长，我们看到当前占主导地位的计算机内存技术（“DRAM”）的容量增长速度放缓。这种不断扩大的差距导致对大容量内存系统的需求，这些系统要么在网络上分解DRAM内存组件，要么采用比DRAM容量更高但性能较慢的内存技术。在单个计算节点内将更大的内存划分为不同的性能层，这种趋势带来了当前内存管理机制和技术无法跟上的挑战。关键的挑战包括如何描述应用程序和工作负载的内存行为，以及如何、何时、何地放置和迁移具有低性能、低能耗和低开销的数据。该研究项目将探索这些基本挑战，并为这些大规模分层存储系统开发和评估解决方案。为了达到最有效的效果，这些解决方案将涵盖计算机硬件（即处理器和内存模块）和系统软件（即操作系统）。结合硬件软件的研究方法，以及提出的解决方案的持续原型设计，确保目标挑战是真实的，并且解决方案不仅对学术界，而且对工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潜力的先决条件。反过来，这些技术对于实现应用程序所需的性能水平，同时保持低成本（货币和环境成本）是必要的，尤其是对于云计算而言。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服务于更高的应用程序负载，从而减少计算和内存的碳足迹。其他社会效益包括这个软硬件研究项目将为学生提供独特的培训，包括本科生和研究生。该项目也极有可能扩大计算机领域的参与。该项目的主要调查人员有为女学生提供建议的记录；参与大学致力扩大参与范围；学生招聘环境得益于大量学生来自历史上在计算机技术领域代表性不足的群体。德克萨斯大学奥斯汀分校是一所公认的西班牙裔大学。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。

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.