SHF: Small: Redesigning Manycore Computer Architecture for the Mega-core Data Center

计算的趋势倾向于将计算转移到称为数据中心的集中位置。这种集中化之所以发生，是因为计算的聚合允许更好地分摊管理计算机的人员、计算设备购买的规模经济，以及改进的电力输送、建筑和冷却的规模经济。再加上这些好处，运行大型数据中心的公司已经开始将多余的计算能力出租给较小的公司，从而使小型公司能够体验到高质量和高可用性计算的好处。小公司可以利用这些资源在互联网上实现爆炸式增长，而无需部署自己的数据中心。目前的数据中心通常使用为台式机和笔记本电脑设计的普通计算机芯片。相比之下，本研究探讨的是如何优化专门针对数据中心的计算机芯片。此外，本研究还探讨了如何修改计算机芯片本身，以便在大型数据中心提供商的不同客户之间更有效地共享资源，以及如何修改计算机芯片架构，以支持诸如计算机资源租赁等新的经济模式。这项工作很重要，因为它将降低创建大型数据中心的成本，使大型共享数据中心的计算资源计费更加透明，并使数据中心更加节能。本研究针对构建针对基础设施即服务（IaaS）云计算系统优化的多核处理器的几个关键挑战进行了攻击。这项工作通过允许来自一个处理器核心的资源被另一个处理器核心利用，打破了多核系统中核心之间的任意边界。这可以使提供给特定虚拟机的硬件资源与特定虚拟机的需求相匹配。此外，这项工作还研究了如何优化数千个独立数据和指令流的缓存层次结构，以实现更高效的内存层次结构，并描述了在多个特定于云的应用程序之间共享时如何利用不同的体系结构资源。

Trends in computing have favored moving computation into centralized locations, called data centers. This centralization is occurring because the aggregation of computing allows better amortization of personnel managing the computers, economies of scale in computing equipment purchasing, and improved economies of scale in power delivery, buildings, and cooling. Coupled with these benefits, companies that run large data centers have begun leasing spare computational capacity to smaller companies, thereby enabling small companies to experience the benefits of high quality and high availability computing. Small companies can leverage these resources to have explosive growth on the Internet without the need to deploy data centers of their own. Current data centers commonly use commodity computer chips designed for desktop and laptop computers. This research, in contrast, explores how to optimize computer chips specifically for the data center. In addition, this research examines how the computer chip itself can be modified to enable more efficient sharing of resources between different customers of large data center providers and how computer chip architecture can be modified to support new economic models such as leasing of computer resources. This work is important because it will decrease the cost of creating large data centers, enable better transparency in billing for computational resources in large-scale shared data centers, and allow data centers to be more energy efficient.This research attacks several of the key challenges in building manycore processors optimized for Infrastructure as a Service (IaaS) Cloud computing systems. This work breaks down arbitrary boundaries between cores in a manycore system by allowing resources from one processor core to be utilized by another processor core. This can enable the hardware resources provided to a particular virtual machine to be matched to the needs of that particular virtual machine. In addition, this work is investigating how to optimize cache hierarchies for thousands of independent data and instruction streams to enable more efficient memory hierarchies, and this work is characterizing how different architectural resources are utilized when shared between multiple Cloud-specific applications.