CAREER: Near-Memory Datacenter Network

职业：近内存数据中心网络

• 批准号: 2239020
• 负责人: Mohammad Alian
• 金额: $ 53.31万
• 依托单位: University of Kansas Center for Research Inc
• 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: 2023-05-01 至 2028-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239020&HistoricalAwards=false
• 关键词: CAREER; Near; Memory; Datacenter; Network

Datacenters are the backbone of today’s digital world that power all daily cloud services. Over the past decade, the exponential increase in the data volumes resulted in computing specialization and over a 100x increase in datacenter network bandwidth utilization. Nevertheless, the network data delivery path to the compute nodes remained unchanged. As such, the current datacenter network suffers from the overhead of the multi-layered software stack, complex network protocol processing, frequent data movement, and network device management. The project novelty is to leverage hardware specialization and near-data processing to re-architect a datacenter’s network. The project's educational plans include organizing yearly outreach workshops to educate K-12 teachers on computing fundamentals, involving underrepresented, undergraduate, and low-income students in research, and integrating research into the higher-education curriculum. Such activities boost student enrollment in higher-educational institutions, train diverse students who deeply understand computer architecture and systems fundamentals, and can innovate across architecture, networking, and operating systems fields. The project’s impacts are to shape the future of datacenter networking and educate a high-quality workforce to supply the needs of the US IT industry and academia.The project aims to design, implement, and evaluate a network data plane that delivers data directly from top-of-rack switch ports to the server’s Central Processing Unit (CPU) caches and memory modules, leveraging an optical interconnection network. The investigator extends the CPU Instruction Set Architecture (ISA) with several network access instructions that enable a process to access the network with minimal software activity. The memory access instructions offload the notification between the CPU and the network interfaces to the hardware. The investigator develops libraries and transport protocols that utilize the proposed network data and control plane to provide robust connectivity between processes running on different servers. Lastly, the project explores the design space of domain-specific near-memory accelerators for network protocol acceleration. The potentially transformative network architecture will enable compute nodes to efficiently sustain tera-bit-per-second connectivity with bare metal network latency numbers. The investigator uses the research findings and evaluation infrastructure to boost the home institution's computer architecture and systems pedagogy.This project is jointly funded by the Software and Hardware Foundations (SHF) core research program in the Computing and Communication Foundations (CCF) Division and the Established Program to Stimulate Competitive Research (EPSCoR).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.

数据中心是当今数字世界的支柱，为所有日常云服务提供动力。在过去的十年中，数据量的指数级增长导致了计算专业化，数据中心网络带宽利用率增加了 100 倍以上。因此，当前的数据中心网络面临着多层软件堆栈的开销、复杂的网络协议处理、频繁的数据移动和网络设备管理的问题。数据该项目的教育计划包括每年组织一次外展研讨会，对 K-12 教师进行计算机基础知识、代表性不足的本科生和低收入学生的研究教育，并将研究纳入高等教育课程。此类活动提高了高等教育机构的学生入学率，培养了深入了解计算机体系结构和系统基础知识并能够在体系结构、网络和操作系统领域进行创新的多元化学生。该项目的影响是塑造计算机的未来。数据中心网络并培养高素质劳动力，以满足美国 IT 行业和学术界的需求。该项目旨在设计、实施和评估网络数据平面，将数据直接从架顶交换机端口传输到服务器的中央处理单元 (CPU) 缓存和内存模块，利用光学互连网络扩展 CPU 指令集架构 (ISA)，使进程能够以最少的软件活动访问网络。卸载CPU和之间的通知研究人员开发了库和传输协议，利用所提出的网络数据和控制平面在不同服务器上运行的进程之间提供强大的连接。潜在的变革性网络架构将使计算节点能够有效地维持每秒太比特的连接和裸机网络延迟数字。研究人员利用研究结果和评估基础设施来提升家庭机构的计算机架构和系统。该项目由计算和通信基金会 (CCF) 部门的软件和硬件基金会 (SHF) 核心研究计划以及刺激竞争研究既定计划 (EPSCoR) 联合资助。该奖项反映了 NSF 的法定使命，并已通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。