2 citations · 2 across the 4 of their papers we have counts for
4 papers
OptiNIC: A Resilient and Tail-Optimal RDMA NIC for Distributed ML Workloads
Ertza Warraich, Ali Imran, Annus Zulfiqar +3
As distributed machine learning (ML) workloads scale to thousands of GPUs connected by high-speed interconnects, tail latency in collective communication has become a major bottlen…
Reimagining RDMA Through the Lens of ML
Ertza Warraich, Ali Imran, Annus Zulfiqar +3
As distributed machine learning (ML) workloads scale to thousands of GPUs connected by ultra-high-speed inter-connects, tail latency in collective communication has emerged as a pr…
SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate
Murayyiam Parvez, Annus Zulfiqar, Roman Beltiukov +4
Machine learning (ML) is increasingly being deployed in programmable data planes (switches and SmartNICs) to enable real-time traffic analysis, security monitoring, and in-network…
Homunculus: Auto-Generating Efficient Data-Plane ML Pipelines for Datacenter Networks
Tushar Swamy, Annus Zulfiqar, Luigi Nardi +2
Support for Machine Learning (ML) applications in networks has significantly improved over the last decade. The availability of public datasets and programmable switching fabrics (…