Towards Efficient GPU Interconnect for AI-centric Systems

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


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

By

Mr. Zhenghang REN


Abstract:

The rapid growth of AI applications on GPUs has significantly increased the 
demand for efficient GPU interconnect. The AI applications rely on the 
interconnect to transmit large volumes of data during model training, 
serving, and protecting sensitive information through interactive 
cryptographic operations. However, existing GPU interconnect suffers from 
limited bandwidth, in-network congestion, and suboptimal data path. These 
drawbacks hinder the communication performance in distributed AI applications 
when data transmission becomes the major bottleneck.

This thesis explores novel solutions to enhance GPU interconnect efficiency 
in model training, serving, and privacy protection mechanisms. It makes the 
following three key contributions: First, we propose FuseLink to maximize GPU 
communication bandwidth by multiplexing network interfaces efficiently with 
both intra- and inter-server connections, mitigating communication 
bottlenecks in traffic-imbalance serving. Second, we introduce MCC, a novel 
congestion control scheme that prevents excessive rate reduction in 
traditional congestion control algorithms by leveraging message-level 
congestion signals, improving communication efficiency and resiliency. 
Finally, we present CORA, a high-performance GPU communication framework that 
incorporates Remote Direct Memory Access (RDMA) with privacy protection 
primitives, such as secret sharing, enabling low-latency, privacy-preserving 
model training and serving across GPU clusters.

Together, these contributions advance the state of GPU interconnect 
protocols, addressing communication efficiency challenges in AI systems.


Date:                   Thursday, 27 November 2025

Time:                   2:00pm - 4:00pm

Venue:                  Room 5501
                        Lifts 25/26

Chairman:               Prof. Yang GAO (MAE)

Committee Members:      Prof. Kai CHEN (Supervisor)
                        Prof. Song GUO
                        Dr. Binhang YUAN
                        Prof. Wei ZHANG (ECE)
                        Dr. Lei YANG (PolyU)