Scaling Deep Learning and Datacenter Applications with Programmable Networks

Prof. Marco Canini KAUST Wednesday, June 1, 2022 @ 11:30 am Room BC 410 Hosted by: Prof. Rachid Guerraoui

Abstract

A wide range of datacenter workloads, including distributed deep learning and key-value storage systems, continue to push the boundaries of efficient system design with low-latency and intensive communication requirements. We propose to address these challenges by extending the reach of application logic into network behavior tailored to meet such requirements. This talk reviews our recent research efforts in pursuit of this objective. Thanks to inroads in programmable networks, we design new in-network capabilities such as in-network aggregation and flexible RDMA offloads that directly accelerate workloads without burdening end users.

Bio

Marco does not know what the next big thing will be. But he’s sure that our next-gen computing and networking infrastructure must be a viable platform for it. Marco’s research spans a number of areas in computer systems, including distributed systems, large-scale/cloud computing and computer networking with emphasis on programmable networks. His current focus is on designing better systems support for AI/ML and providing practical implementations deployable in the real-world.

Marco is an associate professor in Computer Science at KAUST. Marco obtained his Ph.D. in computer science and engineering from the University of Genoa in 2009 after spending the last year as a visiting student at the University of Cambridge. He was a postdoctoral researcher at EPFL and a senior research scientist at Deutsche Telekom Innovation Labs & TU Berlin. Before joining KAUST, he was an assistant professor at UCLouvain. He also held positions at Intel, Microsoft and Google.