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中的新兴软件应用程序必须处理增加数据量。此类数据密集型应用程序强调了计算机系统的通信和存储部分，而不是计算零件。然而，今天的大多数系统是“以计算为中心的？从某种意义上说，他们的设计着重于提高计算效率。计算近数据或处理 - 记忆可以大大减少记忆和处理器之间的数据通信，从而有可能显着提高下一代计算系统的能源效率。 该项目调查并解决了下一代以内存处理器设计面临的挑战？专门处理NEAR-MEMORY（PNM）架构。 该项目将表征未来应用程序的能量和时间行为，并使用此理解来评估如何最佳建筑师新系统结合内存和计算，以紧密的距离。具体而言，这些活动将解决i）编程模型，ii）处理 - 内存架构以及iii）功能和热管理，并随着跨切换数据移动，编译器和微体系构造优化的开发。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，洞察力和结果将作为模块集成到计算机架构中的主流课程中。调查人员还将继续参加研究所外展计划，该计划的目标是促进本科，少数群体和女学生参与工程研究和高等教育。