Keynote Speakers

Oct. 15 (Tuesday) Converged Cloud Networking in the Age of (AI) Computing – The Tale of Ultra Ethernet

Torsten Hoefler (Professor of Computer Science at ETH Zurich)

Abstract: Artificial Intelligence (AI) is revolutionizing Cloud Computing and Networking. This keynote will explore Acceleration as a Service (XaaS), a paradigm providing GPU support for AI and High-Performance Computing (HPC) workloads in cloud environments. XaaS empowers communities to manage and distribute containerized high-performance software efficiently across multiple clouds. We will highlight the critical role of network performance, introducing a novel method to measure the latency sensitivity of applications. This sets the stage for an in-depth analysis of AI workloads in networked systems, revealing the immense demands these applications place on current infrastructure. Today’s Ethernet-based datacenter and cloud networks fall short in supporting large-scale AI training, inference, and HPC workloads. To address this, we will present Ultra Ethernet, an emerging industry standard poised to elevate Ethernet as the ideal interconnect for AI and HPC cloud environments.

Bio: Torsten Hoefler is a Professor of Computer Science at ETH Zurich, a member of Academia Europaea, and a Fellow of the ACM and IEEE. Following a “Performance as a Science” vision, he combines mathematical models of architectures and applications to design optimized computing systems. Before joining ETH Zurich, he led the performance modeling and simulation efforts for the first sustained Petascale supercomputer, Blue Waters, at the University of Illinois at Urbana-Champaign. He is also a key contributor to the Message Passing Interface (MPI) standard where he chaired the "Collective Operations and Topologies" working group. Torsten won best paper awards at ACM/IEEE Supercomputing in 2010, 2013, 2014, 2019, 2022, 2023, and at other international conferences. He has published numerous peer-reviewed scientific articles and authored chapters of the MPI-2.2 and MPI-3.0 standards. For his work, Torsten received the IEEE CS Sidney Fernbach Memorial Award in 2022, the ACM Gordon Bell Prize in 2019, the ISC Jack Dongarra award, the IEEE TCSC Award of Excellence (MCR), ETH Zurich's Latsis Prize, the SIAM SIAG/Supercomputing Junior Scientist Prize, the IEEE TCSC Young Achievers in Scalable Computing Award, and the BenchCouncil Rising Star Award. Following his Ph.D., he received the 2014 Young Alumni Award and the 2022 Distinguished Alumni Award of his alma mater, Indiana University. Torsten was elected to the first steering committee of ACM's SIGHPC in 2013 and he was re-elected for every term since then. He was the first European to receive many of those honors; he also received both an ERC Starting and Consolidator grant. His research interests revolve around the central topic of performance-centric system design and include scalable networks, parallel programming techniques, and performance modeling for large-scale simulations and artificial intelligence systems. Additional information about Torsten can be found on his homepage at htor.inf.ethz.ch.

Oct. 16 (Wednesday) Enabling Distributed Intelligent Services at the Network Edge

Leandros Tassiulas (John C. Malone Professor of Electrical & Computer Engineering at Yale University)

Abstract: In order to cope with the vast amount of data and processing requirements of distributed intelligence services novel synergies between networking and AI need to be leveraged; I will present several recent results in that area. First we look into the joint optimization of service placement and request routing in dense multi-cell wireless networks. The storage requirements of the different services as well as the processing and communication requirements of the service requests are considered. An algorithm that achieves close-to-optimal performance using a randomized rounding technique is presented. Chains of virtual network functions are considered then and allocation algorithms that comply to the related precedence constraints are presented. Distributed learning is considered then as a representative AI service at the network edge. An algorithm that addresses the heterogeneity of the different clients is presented towards providing unbiased services. Then we look into the issue of limited network resources for training as well as the asymmetric access profiles of the different clients and provide an algorithm for client selection to mitigate the effect of asymmetries. Finally we will present some recent results on combining graph Neural Networks (NN) and transformers for spatio-temporal networks predictions and optimization.

Bio: Leandros Tassiulas is the John C. Malone Professor of Electrical & Computer Engineering at Yale University, where he served as department head 2016-2022. His current research is on intelligent services and architectures at the edge of next generation networks including Internet of Things, sensing & actuation in terrestrial and non terrestrial environments and quamtum networks. He worked in the field of computer and communication networks with emphasis on fundamental mathematical models and algorithms of complex networks, wireless systems and sensor networks. His most notable contributions include the max-weight scheduling algorithm and the back-pressure network control policy, opportunistic scheduling in wireless, the maximum lifetime approach for wireless network energy management, and the consideration of joint access control and antenna transmission management in multiple antenna wireless systems. Dr. Tassiulas is a Fellow of IEEE (2007) and of ACM (2020) as well as a member of Academia Europaea (2023). His research has been recognized by several awards including the IEEE Koji Kobayashi computer and communications award (2016), the ACM SIGMETRICS achievement award 2020, the inaugural INFOCOM 2007 Achievement Award “for fundamental contributions to resource allocation in communication networks,” several best paper awards including the INFOCOM 1994, 2017 and Mobihoc 2016, a National Science Foundation (NSF) Research Initiation Award (1992), an NSF CAREER Award (1995), an Office of Naval Research Young Investigator Award (1997) and a Bodossaki Foundation award (1999). He holds a Ph.D. in Electrical Engineering from the University of Maryland, College Park (1991) and a Diploma of Electrical Engineering from Aristotle University of Thessaloniki, Greece. He has held faculty positions at Polytechnic University, New York, University of Maryland, College Park, University of Ioannina and University of Thessaly, Greece.