CAREER: MemMax: Maximizing Cyberinfrastructure Memory Utilization via Hardware Acceleration for OS-level Memory Utilization Management

职业：MemMax：通过操作系统级内存利用率管理的硬件加速最大化网络基础设施内存利用率

• 批准号: 1942590
• 负责人: Xun Jian
• 金额: $ 51.71万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942590&HistoricalAwards=false
• 关键词: CAREER; MemMax; Maximizing; Cyberinfrastructure; Memory

The performance growth of cyberinfrastructure systems, such as supercomputers and computing data centers, is important to US economy and well-being; it helps boost scientific and engineering productivity, which is vital for US global competitiveness and helps grow the US digital economy, which is an important part of overall US economy. Until recently, Moore’s Law guided the exponential growth of hardware resources under the same cost and has been a key driving factor behind cyberinfrastructure performance growth. As the growth predicted by Moore’s Law slows down, sustaining performance gains requires cyberinfrastructure to maximize the utilization of its available hardware resources, especially computing memory. Currently, cyberinfrastructure systems often only utilize up to a small fraction of their memory compared to what is physically and/or theoretically possible. As memory primarily serves as a performance enhancer, memory under-utilization causes under-performance and unnecessary investment in additional computing resources. This project seems to address this problem by identifying ways to better use memory resources in supercomputers and cloud computing systems, thereby increasing the efficiency and performance of these systems. The project, MemMax, actively involves both graduate and undergraduate students, along with outreach to K-12 students.MemMaX explores how to co-design CPU and OS to maximize memory utilization in cyberinfrastructure systems to boost their performance both user-transparently and substantially by a factor of up to 4. MemMax consists of two research thrusts, one targeting HPC systems and targeting cloud systems, as these two types of systems have different causes for their memory underutilization. The research methodology consists of real-system measurements to characterize the behavior of existing systems, hardware prototyping to valid the functional correctness of MemMax, and architectural simulations to quantify the performance improvement MemMax achieves.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.

超级计算机和计算数据中心等网络基础设施系统的性能增长对美国经济和福祉至关重要，有助于提高科学和工程生产力，这对美国的全球竞争力至关重要，并有助于发展美国数字经济直到最近，摩尔定律引导着硬件资源在相同成本下呈指数级增长，并且一直是网络基础设施性能增长的关键驱动因素。随着摩尔定律预测的增长放缓，维持性能增长需要。网络基础设施，以最大限度地利用其可用硬件资源，特别是计算内存，目前，与物理和/或理论上可能的内存相比，网络基础设施系统通常仅使用一小部分内存，因为内存主要用作性能增强器。内存利用率不足会导致性能不佳和不必要的额外计算资源投资，该项目似乎通过找到更好地利用超级计算机和云计算系统中的内存资源的方法来解决这个问题，从而提高这些系统的效率和性能。 , 积极地涉及研究生和本科生，以及 K-12 学生。MemMaX 探索如何共同设计 CPU 和操作系统，以最大限度地提高网络基础设施系统中的内存利用率，从而以用户透明的方式大幅提高其性能高达 4 倍MemMax 包含两个研究重点，一是针对 HPC 系统，二是针对云系统，因为这两类系统造成内存利用率不足的原因不同，研究方法包括真实系统测量，以表征现有系统和硬件的行为。原型设计以验证 MemMax 的功能正确性，以及架构模拟以量化 MemMax 所实现的性能改进。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。

项目成果

Translation-optimized Memory Compression for Capacity

针对容量进行翻译优化的内存压缩

• DOI: 10.1109/micro56248.2022.00073
• 发表时间: 2022-10
• 期刊: 2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO
• 作者: Panwar, Gagandeep;Laghari, Muhammad;Bears, David;Liu, Yuqing;Jearls, Chandler;Choukse, Esha;Cameron, Kirk W.;Butt, Ali R.;Jian, Xun
• 通讯作者: Jian, Xun

Quantifying Server Memory Frequency Margin and Using It to Improve Performance in HPC Systems

量化服务器内存频率裕度并使用它来提高 HPC 系统的性能

• DOI: 10.1109/isca52012.2021.00064
• 发表时间: 2021-06
• 期刊: The 48th Annual International Symposium on Computer Architecture (ISCA
• 作者: Zhang, Da;Panwar, Gagandeep;Kotra, Jagadish B.;DeBardeleben, Nathan;Blanchard, Sean;Jian, Xun
• 通讯作者: Jian, Xun

Quantifying Memory Underutilization in HPC Systems and Using it to Improve Performance via Architecture Support

量化 HPC 系统中的内存利用率不足并通过架构支持利用它来提高性能

• DOI: 10.1145/3352460.3358267
• 发表时间: 2019-10-12
• 期刊: Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture
• 作者: Gag;eep Panwar;eep;Da Zhang;Yihan Pang;M. Dahshan;Nathan Debardeleben;B. Ravindran;Xun Jian
• 通讯作者: Xun Jian