CAREER: Leveraging temporal streams for micro-architectural innovation in data center servers

职业：利用时间流进行数据中心服务器的微架构创新

• 批准号: 1452904
• 负责人: Michael Ferdman
• 金额: $ 39.76万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-15 至 2021-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1452904&HistoricalAwards=false
• 关键词: CAREER; Leveraging; temporal; streams; micro

As the global user base for online services continues to expand and new services and features are rapidly developed, data centers from which these online cloud services operate experience constant pressure to achieve higher performance and improve their quality of service. However, supporting the adoption of cloud services in all aspects of people?s daily lives requires expanding data centers to an extreme scale, with hundreds of millions of servers and ecologically unthinkable energy bills.  This research develops technologies to improve the performance and efficiency of future data centers, targeting higher performance and lower energy costs from each deployed server.  As such, it directly contributes to sustainable growth of data centers and online services, while at the same time training world-class experts specialized in tackling the challenges facing future data centers and clouds.This research leverages a recently-codified phenomenon called "temporal streams" to solve a number of long-standing micro-architectural performance bottlenecks facing server systems in the cloud.  Many of the performance enhancing techniques developed over the course of the past several decades for the desktop, mobile, and super-computer domains provide limited benefits to server systems, because the size and complexity of a typical cloud workload requires significantly greater meta-data storage capacity than currently available to these techniques.  This work re-architects the meta-data storage of speculative structures, leveraging temporal streams to expand their effective capacity.  Specifically, this work targets instruction prefetchers, branch predictors, and hardware memorization as case studies to demonstrate the ability of temporal streaming to provide sufficient meta-data storage for these mechanisms when executing cloud workloads.

随着全球在线服务用户群的不断扩大以及新服务和功能的快速开发，运营这些在线云服务的数据中心不断面临着实现更高性能和提高服务质量的压力。然而，支持人们在各个方面采用云服务？的日常生活需要将数据中心扩展到一个极端的规模，数以亿计的服务器和生态上无法想象的能源账单。这项研究开发了技术，以提高未来数据中心的性能和效率，目标是提高每个部署的服务器的性能和降低能源成本。因此，它直接有助于数据中心和在线服务的可持续增长，同时培养世界一流的专家，专门应对未来数据中心和云所面临的挑战。这项研究利用最近编纂的称为“时间流”的现象来解决一些长期存在的微观问题，云服务器系统面临的架构性能瓶颈。在云计算过程中开发的许多性能增强技术，过去几十年来，桌面、移动的和超级计算机领域为服务器系统提供了有限的好处，因为典型云工作负载的大小和复杂性需要比这些技术当前可用的元数据存储容量大得多的元数据存储容量。这项工作重新构建了推测性结构的元数据存储，利用时间流来扩展其有效容量。具体而言，该工作将指令预取器、分支预测器和硬件存储器作为案例研究的目标，以证明在执行云工作负荷时临时流传输为这些机制提供足够的元数据存储的能力。