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: 2312508
• 负责人: Tong Zhang
• 金额: $ 33.4万
• 依托单位: Rensselaer Polytechnic Institute
• 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=2312508&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.

现代数据中心的应用程序正变得越来越多，并受到传统冯·诺伊曼体系结构的局限性的严格限制。新兴的Compute Express Link（CXL）的硬件生态系统为实现了彻底的新的内存计算范式提供了前所未有的机会，该计算范式可以有效地减轻von Neumann瓶颈，以用于未来的内存中心应用。但是，为了充分释放这项新兴技术的潜力，必须充分解决一些基本的研究挑战。该研究项目采用一种整体和凝聚力的方法来开发解决方案，以应对挑战，为内存计算基础架构的未来铺平了道路，并从根本上影响了以内存为中心的应用程序。此外，该项目的学术活动通过向学生提供培训机会，丰富课程和课堂教学以及为教育和推广计划做出贡献，从而扩展了其影响。 该项目率先进行基础研究，旨在克服三个关键的挑战，这些挑战阻碍了内存计算的实现：零散的内存资源，对内存共享的体系结构支持不足以及硬件和软件模型之间的效率低下。利用新兴的CXL技术，该项目采用了系统的设计方法来解决这些复杂的问题。它涉及跨越系统堆栈中多个层的全面努力，其中包括高级硬件功能的开发，系统中内存资源利用的优化以及将硬件支持集成到一般的编程平台中以加快应用程序。该项目进行的研究为基础研究奠定了基础，该研究迎合了以未来以记忆为中心的计算领域对数据密集型应用的紧迫要求。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。