XPS: FULL: CCA: Cymric: A Flexible Processor-Near-Memory System Architecture

XPS：完整：CCA：Cymric：灵活的处理器近内存系统架构

• 批准号: 1533767
• 负责人: Hyesoon Kim
• 金额: $ 75万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1533767&HistoricalAwards=false
• 关键词: XPS; FULL; CCA; Cymric; Flexible

Emerging software applications in data analytics, graph analysis, and machine learning must process increasing volumes of data. Such data-intensive applications stress the communication and storage parts of a computer system more than the computation parts. Yet most of today?s systems are ?compute-centric? in the sense that their designs focus on improving the efficiency of computation. Computing near data or processing-near-memory can significantly reduce data communication between memory and processor providing potential for significant improvement in energy-efficiency of next generation computing systems. This project investigates and addresses the challenges facing the design of the next generation of memory-centric processors ? specifically processing-near-memory (PNM) architectures. The project will characterize the energy and time behavior of future applications and use this understanding to assess how to best architect new systems that combine memory and compute in close proximity. Specifically, the activities will address i) programming models, ii) processing-near-memory architectures, and iii) power & thermal managements with the development of cross-cutting data movement, compiler and microarchitectural optimizations. The results of this work can influence the design of future enterprise and high performance computing systems. The major principles, insights, and outcomes of this project will be integrated as modules into mainstream courses in Computer Architecture. The investigators will also continue their participation in Institute outreach programs that have the goal of promoting participation of undergraduate, minority, and female students in engineering research and higher education.

数据分析、图形分析和机器学习方面的新兴软件应用程序必须处理越来越多的数据。这种数据密集型应用程序强调计算机系统的通信和存储部分，而不是计算部分。今天大部分？S系统是？以计算机为中心？在这个意义上，他们的设计集中在提高计算效率。计算近数据或处理近存储器可以显著减少存储器与处理器之间的数据通信，从而提供下一代计算系统的能量效率的显著改进的潜力。 这个项目调查和解决面临的挑战，设计下一代的内存为中心的处理器？特别是处理近存储器（PNM）架构。 该项目将描述未来应用程序的能量和时间行为，并使用这种理解来评估如何最好地构建将联合收割机内存和计算紧密结合的新系统。具体而言，这些活动将解决i）编程模型，ii）处理近内存架构，iii）电源热管理，以及跨领域数据移动，编译器和微架构优化的发展。这项工作的结果可能会影响未来的企业和高性能计算系统的设计。该项目的主要原则，见解和成果将作为模块集成到计算机体系结构的主流课程中。研究人员还将继续参与研究所的外展计划，其目标是促进本科生，少数民族和女学生参与工程研究和高等教育。