CAREER:In-Network Computation Meets Data Persistence

In this current data-centric era, data generated by social media, video sharing applications, swarms of sensors, autonomous cars, etc. is growing exponentially. Unfortunately, as the technology scaling slows down, the semiconductor industry has been facing a major challenge in providing better performance while processing such large datasets. As a result, it is essential to redesign current systems to sustain the demand for computing over exponentially growing datasets.

However, the fundamental model of computing has not changed over many decades. In our current Von Neumann model, data sits in the slower persistent storage and it is moved back and forth to faster memory for computation by the processor. This project focuses on enabling a paradigm shift in how data is stored and processed in future systems.

Data should be processed close to where data is generated and data should be persisted where it is used. The goal of this work is to fundamentally rethink how persistent states are maintained in data centers and expose the persistency domain in the network for better performance, throughput, and tail latency. This work redesigns programmable switches and smart network interface cards (NICs) to manipulate persistent data in the network.

This project enables faster execution of applications that manipulate global persistent state, such as global-scale deep learning training. Machine learning models have a huge societal impact and they are being used to predict cancers, epidemics, etc. This research will potentially impact numerous scientific fields to take a leap forward towards new innovations.

The datasets, tools, and techniques developed in this research will be made publicly available to benefit the whole community. The educational part of this project will focus on attracting undergraduate and high-school students towards science and technology through a summer program.

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.