Collaborative Research: SHF: Medium: Hardware and Software Support for Memory-Centric Computing Systems

协作研究：SHF：中：以内存为中心的计算系统的硬件和软件支持

• 批准号: 2312507
• 负责人: Xiaodong Zhang
• 金额: $ 33.3万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-12-15 至 2026-11-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312507&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Medium; Hardware

Modern data-center applications are becoming increasingly memory-intensive and severely constrained by the limitations of the traditional von Neumann architecture. The emerging hardware ecosystem of Compute Express Link (CXL) opens an unprecedented opportunity to enable a radically new in-memory computing paradigm that can effectively mitigate the von Neumann bottleneck for future memory-centric applications. However, to fully unlock the potential of this emerging technology, several fundamental research challenges must be adequately addressed. This research project takes a holistic and cohesive approach to develop solutions that tackle the challenges head-on, paving the way for the future of in-memory computing infrastructure and fundamentally impacting memory-centric applications. Furthermore, the academic activities in this project extend their impact by providing training opportunities to students, enriching curriculum and classroom teaching, and contributing to educational and outreach initiatives. This project spearheads the fundamental research aimed at overcoming three pivotal challenges that hinder the realization of in-memory computing: fragmented memory resources, insufficient architectural support for memory sharing, and inefficient separation between hardware and software models. Leveraging the emerging CXL technology, this project adopts a systematic design methodology to address these intricate issues. It involves comprehensive efforts spanning multiple layers within the system stack, featuring the development of advanced hardware functionalities, the optimization of memory resource utilization in the system, and the integration of hardware support into general programming platforms to expedite applications. The research undertaken in this project lays the groundwork for fundamental studies that cater to the pressing demands of data-intensive applications in the realm of future memory-centric computing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

现代数据中心应用程序正变得越来越内存密集，并受到传统冯·诺依曼架构的严重限制。计算快速链接（CXL）的新兴硬件生态系统为实现全新的内存计算范式提供了前所未有的机会，可以有效缓解未来以内存为中心的应用的冯·诺伊曼瓶颈。然而，为了充分释放这一新兴技术的潜力，必须充分解决几个基础研究挑战。该研究项目采用整体和内聚的方法来开发解决方案，以正面应对挑战，为内存计算基础设施的未来铺平道路，并从根本上影响以内存为中心的应用程序。此外，该项目的学术活动通过为学生提供培训机会、丰富课程和课堂教学、促进教育和外展活动来扩大其影响。这个项目是基础研究的先锋，旨在克服阻碍内存中计算实现的三个关键挑战：碎片化的内存资源，对内存共享的架构支持不足，以及硬件和软件模型之间的低效分离。利用新兴的CXL技术，该项目采用系统的设计方法来解决这些复杂的问题。它涉及系统堆栈内跨越多层的综合努力，具有先进硬件功能的开发，系统中内存资源利用率的优化，以及将硬件支持集成到通用编程平台中以加快应用程序。在这个项目中进行的研究为基础研究奠定了基础，以满足未来以内存为中心的计算领域中数据密集型应用的迫切需求。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。