IoT Thrust Seminar | Accelerating Distributed Systems with In-Network Computation
With Moore's Law slowing down, building distributed and heterogeneous systems becomes a new trend to support large-scale applications, such as large model training and big data analytics. In-Network Computing (INC) is an effective approach to building such distributed systems. INC leverages programmable network devices to process traversing data packets, and provides line-rate and low-latency data processing capabilities, which could compress traffic volume and accelerate the overall transmission and job efficiency. In this talk, we will share the progress and development of INC technologies, including INC protocol design for machine learning and data analytics, and RDMA-compatible INC solutions. These works are published in NSDI21 and ASPLOS23.
Prof. Wenfei Wu is an assistant professor from the School of Computer Science at Peking University. He obtained his Ph.D. degree from the University of Wisconsin-Madison in 2015. Dr. Wu researches into computer networks and distributed systems, and has published more than 50 papers in these areas. Dr. Wu's recent research focus is to build in-network computation (INC) methods for distributed systems; his work on INC-empowered distributed machine learning system ATP won the best paper award in NSDI 2021, and that on INC-empowered distributed data analytics system ASK won the distinguished paper award in ASPLOS 2023; Dr. Wu won other awards like IPCCC best paper runner-up in 2019, SoCC best student paper in 2013, etc.