CAREER: Commonality Exploiting Architectures for Energy Efficiency

越来越多的计算正在转移到云和大型数据中心。托管的web应用程序，如社交网络、托管的电子邮件和照片共享，都是迁移到云中的应用程序的例子。为了控制成本和节约能源，最大化数据中心计算机的计算效率是至关重要的。这个研究项目通过寻找在云中执行的所有应用程序之间的共性来调查数据中心处理器的计算效率。该项目探索如何通过利用云中的应用程序之间可用的通用性来改变微处理器核心以节省能源。此外，该项目利用综合教育计划，通过使用大规模开放在线课程（MOOC），在大学内部、整个纽约地区和全世界传播有关计算机体系结构的信息。加速数据中心和大数据应用可以帮助人们了解已收集的数据，从而能够了解社会趋势和公共卫生挑战，从而产生巨大的社会影响。该研究项目的成功将创造更高效的数据中心，使人类能够回答社会大数据问题，并拥有更丰富的互联网体验。该项目研究如何创建计算机体系结构，以利用在数据中心不同核心上执行的程序之间的相似性。一个软件系统可以识别出可能表现出共性的候选者。一旦确定了可能的共性，微架构信息就会在不同的核心之间或在单个核心内发送，以使一个程序的执行能够减少执行第二个类似程序所需的能量。这个项目研究在执行公共程序的内核之间共享什么是最好的微体系结构信息，以及如何在内核之间发送这些信息是最好的。特别地，这个项目研究了定制专门用于发送通用信息的片上网络，以及如何修改多核处理器来获取通用的微体系结构信息。

An increasing amount of computation is moving into the Cloud and large-scale data centers. Hosted web-applications, such as social networks, hosted email, and photo sharing, are all examples of applications, which have moved into the Cloud. In order to control costs and save energy, maximizing the computational efficiency of datacenter computers is of paramount importance. This research project investigates computational efficiency in datacenter processors by looking for commonality between all of the applications that are executing in the Cloud. This project explores how to change a microprocessor core to save energy by exploiting the commonality available between applications in the Cloud. In addition, this project utilizes an integrated education plan that will disseminate information about computer architecture inside of the University, across the New York region, and across the world through the use of a Massively Open Online Course (MOOC). Accelerating data center and big data applications can have large societal impact by helping humans understand data that has been collected, thus enabling the understanding of social trends and public health challenges. The success of the research project will create more efficient data centers that will enable humanity to answer societal big data questions and have richer Internet experiences.This project investigates how to create computer architectures that can exploit similarity between programs executing on different cores across the data center. A software system identifies likely candidates that exhibit commonality. Once likely commonality has been identified, micro-architectural information is sent between different cores or within a single core to enable the execution of one program to reduce the energy needed to execute a second similar program. This project investigates what is the best micro-architectural information to share between cores executing common programs and how it is best to send that information between cores. In particular, this project investigates customizing on-chip networks specialized for sending commonality information and how to modify a manycore processor to harvest common micro-architectural information.