SHF: Small: Workload Characterization and Benchmark Synthesis for Emerging Computing Systems

SHF：小型：新兴计算系统的工作负载表征和基准综合

• 批准号: 1117895
• 负责人: Lizy John
• 金额: $ 42.5万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117895&HistoricalAwards=false
• 关键词: SHF; Small; Workload; Characterization; Benchmark

The face of emerging computing systems is changing rapidly. On one hand computers are looking smaller than ever. The smart phone is nothing but a pocket computer which also has the ability to make a phone call. On the other hand, computers are getting bigger than ever, as in a cloud computing server. These cloud systems are distributed systems with a large number of processors, abundant memory and disks and plentiful network resources and bandwidth, and are huge. This project is on workload characterization and benchmark synthesis for emerging computing systems such as the embedded computer and the cloud computer.The first component of this project consists in characterizing and understanding workloads on emerging computing platforms. Characteristics of embedded computing software in embedded Java or paradigms such as the .NET, or cloud applications such as Hadoop are not well-understood. It is also unclear whether next generation cloud servers should use simple energy efficient processors or more powerful processors geared towards performance. The characterization component of our project is geared towards understanding these issues. The second component of this project consists of benchmark synthesis to create workload proxies that have equivalent performance and energy characteristics as the original applications but without the functionality. These proxy workloads will enable efficient pre-silicon design exploration and sharing of proprietary applications.At a broader level, the research creates mechanisms that enable hardware and software developers to collaborate on joint product development without fear of loss of intellectual property. It also trains several graduate and undergraduate students in an important research area, and influences courses on embedded and cloud computing.

新兴计算系统的面孔正在迅速变化。一方面，计算机看起来比以往任何时候都小。智能手机不过是一台口袋计算机，还可以打电话。另一方面，就像在云计算服务器中一样，计算机比以往任何时候都变得更大。这些云系统是分布式系统，具有大量处理器，丰富的内存和磁盘以及大量的网络资源和带宽，并且非常巨大。该项目是在工作负载表征和基准合成中，用于新兴计算系统（例如嵌入式计算机和云计算机）。该项目的第一个组成部分包括表征和了解新兴计算平台上的工作负载。嵌入式Java或范式（例如.NET）或Hadoop之类的云应用程序中的嵌入式计算软件的特性并不理解。还不清楚下一代云服务器是否应使用简单的节能处理器或针对性能的更强大的处理器。我们项目的表征组成部分旨在理解这些问题。该项目的第二个组成部分是基准合成，以创建具有等效性能和能量特征作为原始应用但没有功能的功能特征的工作负载代理。这些代理工作负载将使专有应用程序有效地进行预硅的设计探索和共享。在更广泛的水平上，该研究创建了机制，使硬件和软件开发人员能够在不担心失去知识产权的情况下协作共同产品开发。它还在重要的研究领域培训了几名研究生和本科生，并影响嵌入式和云计算的课程。