CNS Core: Small: Democratizing Network Hardware Offloads

CNS 核心：小型：网络硬件卸载大众化

• 批准号: 1910796
• 负责人: Srinivas Narayana
• 金额: $ 38.8万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910796&HistoricalAwards=false
• 关键词: CNS; Core; Small; Democratizing; Network

There are many practical applications for large-scale data processing. Examples include weather prediction, scientific computing, and Google searches. While the size of compute clusters has increased over time, the amount of data that can be processed with a single compute server is a tiny fraction of the datasets, placing a large burden on the interconnecting network that moves data among the servers. Academic researchers and industry developers have endeavored to improve the speed of this underlying network. Yet, the engineering effort required at the server side to quickly and flexibly process data arriving over the network remains enormous and out of reach for many application developers.This project aims to significantly broaden the community of application developers benefiting from increasing network speeds. The project leverages recent technological developments in network interface cards (NICs) attached to servers, namely programmable NIC offload, to advance developer-friendly abstractions, programming languages, and platforms. The main technical contributions of this project are (1) an abstract NIC model to represent diverse NIC substrates, and (2) compilation algorithms that translate flexible developer programs into NIC implementations, and provide predictable high throughput and low delay for the traffic processed by the NIC. The result is to enable developers to process data flexibly on server NICs with guarantees of high performance.All software prototypes developed over the course of this project will be released openly and freely to serve as research experimentation platforms and as pedagogical tools. The PI will develop curricular material to train graduate and senior undergraduate students in high-performance network data processing. The project will also introduce research activities to diverse high school student groups.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.

大规模数据处理有许多实际应用。示例包括天气预测，科学计算和Google搜索。虽然计算簇的大小随时间增加，但可以使用单个计算服务器处理的数据量是数据集的很小一部分，在互连网络上增加了巨大的负担，该网络将数据移动数据之间的数据。学术研究人员和行业开发人员已努力提高这个基础网络的速度。然而，服务器端需要快速而灵活地处理网络上的数据的工程工作仍然很大，并且对于许多应用程序开发人员而言。该项目旨在显着扩大应用程序开发人员社区，从而受益于提高网络速度。该项目利用了连接到服务器的网络接口卡（NICS）的最新技术发展，即可编程的NIC卸载，推进开发人员友好的抽象，编程语言和平台。该项目的主要技术贡献是（1）一个抽象的NIC模型，用于代表不同的NIC底物，以及（2）将灵活开发人员程序转化为NIC实施的汇编算法，并为NIC处理的流量提供了可预测的高吞吐量和低延迟。结果是使开发人员能够在服务器NIC上灵活地处理数据，并保证了高性能。在本项目过程中开发的所有软件原型都将公开，自由发布，以充当研究实验平台和教学工具。 PI将开发课程材料，以培训高性能网络数据处理中的毕业生和高级本科生。该项目还将向多样化的高中学生群体介绍研究活动。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评论标准来评估值得支持的。

项目成果

Synthesizing safe and efficient kernel extensions for packet processing

综合安全高效的内核扩展进行数据包处理

• DOI: 10.1145/3452296.3472929
• 发表时间: 2021
• 期刊: ACM SIGCOMM'21
• 作者: Xu, Qiongwen;Wong, Michael D.;Wagle, Tanvi;Narayana, Srinivas;Sivaraman, Anirudh
• 通讯作者: Sivaraman, Anirudh