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.

随着全球在线服务的全球用户基础继续扩展，新服务和功能得到了迅速开发，这些在线云服务从中承受着持续的压力，以实现更高的性能并提高其服务质量。但是，支持在人们日常生活的各个方面都采用云服务需要将数据中心扩展到极端规模，其中数亿服务器和生态上无法想象的能源费用。这项研究开发技术是为了提高未来数据中心的性能和效率，针对每个部署的服务器的较高性能和降低能源成本。 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.在过去的几十年中，台式机，移动和超级计算机域在过去的几十年中开发的许多性能增强技术为服务器系统提供了有限的好处，因为典型的云工作负载的大小和复杂性需要比当前可用的元数据存储容量要大得多。这项工作重新构建了投机结构的元数据存储，利用临时流以扩大其有效能力。具体而言，这项工作针对预摘要，分支预测变量和硬件存储器作为案例研究，以证明临时流媒体在执行云工作负载时为这些机制提供足够的元数据存储的能力。