Towards Efficient and Secure GPU Interconnect for AI-centric Systems

PhD Thesis Proposal Defence


Title: "Towards Efficient and Secure GPU Interconnect for AI-centric Systems"

by

Mr. Zhenghang REN


Abstract:

The rapid growth of GPU-driven AI applications demands high-speed and secure 
GPU interconnects to support large-scale model training and deployment in 
parallel and distributed systems. Existing interconnects, however, face 
significant challenges, including limited bandwidth, in-network congestion, 
and the risk of private data leakage. These issues directly hinder the 
performance, scalability, and security of modern AI systems.

This thesis explores novel solutions to enhance GPU interconnect performance 
while ensuring data confidentiality in AI-centric systems. It makes the 
following three key contributions: First, we propose FuseLink to maximize 
GPU communication bandwidth. By efficiently transmitting data across 
multiple network interfaces, it leverages both intra- and inter-server 
connections to mitigate communication bottlenecks in multi-GPU systems. 
Second, we introduce MCC, a novel congestion control scheme. It improves 
communication efficiency and resilience in AI-centric networks by leveraging 
message-level congestion signals to prevent the excessive rate reduction 
common in traditional algorithms. Finally, we present CORA, a 
high-performance GPU communication framework for secure machine learning. 
This framework incorporates Remote Direct Memory Access (RDMA) with 
cryptographic primitives like secret sharing to enable low-latency, 
privacy-preserving model training and serving across GPU clusters.

Together, these contributions advance the state of GPU interconnect 
protocols, addressing critical challenges in bandwidth, congestion 
management, and security for AI-centric systems.


Date:                   Wednesday, 20 August 2025

Time:                   2:00pm - 4:00pm

Venue:                  Room 3494
                        Lifts 25/26

Committee Members:      Prof. Kai Chen (Supervisor)
                        Prof. Song Guo (Chairperson)
                        Dr. Binhang Yuan