Romaric Duvignau

Erik Bångsbo, Zakaria Hersi, Anna Benktson et al.

Remote Direct Memory Access (RDMA) is a key enabler of high-performance systems, offering low latency, high throughput, and reduced CPU overhead by allowing direct memory-to-memory transfers between machines. However, its design bypasses traditional CPU-mediated security mechanisms, introducing critical vulnerabilities in untrusted environments. This work explores the integration of RDMA and AES-128 encryption to secure data transfers without compromising performance. We implement encryption directly within the data plane of a programmable Tofino switch using the P4 programming language. By offloading encryption from the CPU to the switch, our design preserves RDMA's performance benefits while addressing its security shortcomings. Experimental results show that the system achieves throughput of 0.37 Gbps for 16-byte packets, 0.76 Gbps for 32-byte packets, 1.83 Gbps for 64-byte packets, and 1.9 Gbps for 128-byte packets. These findings demonstrate the feasibility of secure, high-throughput RDMA communication using programmable network hardware.

Yuhang Song, Naima Abrar Shami, Romaric Duvignau et al.

As graphs scale to billions of nodes and edges, graph Machine Learning workloads are constrained by the cost of multi-hop traversals over exponentially growing neighborhoods. While various system-level and algorithmic optimizations have been proposed to accelerate Graph Neural Network (GNN) pipelines, data management and movement remain the primary bottlenecks at scale. In this paper, we explore whether graph sparsification, a well-established technique that reduces edges to create sparser neighborhoods, can serve as a lightweight pre-processing step to address these bottlenecks while preserving accuracy on node classification tasks. We develop an extensible experimental framework that enables systematic evaluation of how different sparsification methods affect the performance and accuracy of GNN models. We conduct the first comprehensive study of GNN training and inference on sparsified graphs, revealing several key findings. First, sparsification often preserves or even improves predictive performance. As an example, random sparsification raises the accuracy of the GAT model by 6.8% on the PubMed graph. Second, benefits increase with scale, substantially accelerating both training and inference. Our results show that the K-Neighbor sparsifier improves model serving performance on the Products graph by 11.7x with only a 0.7% accuracy drop. Importantly, we find that the computational overhead of sparsification is quickly amortized, making it practical for very large graphs.