CNS Core: Small: Democratizing Network Hardware Offloads

大规模数据处理有许多实际应用。例子包括天气预报、科学计算和谷歌搜索。虽然计算集群的大小随着时间的推移而增加，但单个计算服务器可以处理的数据量只是数据集的一小部分，这给在服务器之间移动数据的互连网络带来了很大的负担。学术研究人员和行业开发人员一直在努力提高这一底层网络的速度。然而，服务器端快速灵活地处理通过网络到达的数据所需的工程工作仍然是巨大的，对于许多应用程序开发人员来说是遥不可及的。该项目旨在显著扩大应用程序开发人员社区，使其受益于不断提高的网络速度。该项目利用了服务器上网络接口卡（NIC）的最新技术发展，即可编程网卡卸载，以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模型来表示不同的NIC基板，(2)编译算法，将灵活的开发程序转换为NIC实现，并为NIC处理的流量提供可预测的高吞吐量和低延迟。其结果是使开发人员能够灵活地处理服务器网卡上的数据，并保证高性能。在这个项目过程中开发的所有软件原型都将公开和免费发布，作为研究实验平台和教学工具。PI将开发课程材料，培养研究生和大四本科生在高性能网络数据处理方面的能力。该项目还将向不同的高中学生群体介绍研究活动。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。

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.