CNS Core: Small: Democratizing Network Hardware Offloads

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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.