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

随着在线服务的全球用户群不断扩大，新服务和新功能迅速发展，运营这些在线云服务的数据中心面临着不断提高性能和提高服务质量的压力。然而，在各个方面支持采用云服务的人呢？美国的日常生活需要将数据中心扩展到极端规模，包括数亿台服务器和生态上难以想象的能源账单。本研究开发了提高未来数据中心性能和效率的技术，目标是提高每台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，同时培养世界级的专家，专门应对未来数据中心和云面临的挑战。这项研究利用了最近编纂的一种叫做“时间流”的现象来解决云计算中服务器系统长期面临的微架构性能瓶颈。在过去几十年中为桌面、移动和超级计算机领域开发的许多性能增强技术对服务器系统的好处有限，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大得多的元数据存储容量。这项工作重新构建了投机结构的元数据存储，利用时间流来扩展其有效容量。具体来说，这项工作将指令预取器、分支预测器和硬件记忆作为案例研究，以演示在执行云工作负载时，时序流为这些机制提供足够元数据存储的能力。

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.