As deep learning models and input data continue to scale at an unprecedented rate, it has
become inevitable to move towards distributed training platforms to fit the models and …

Remote direct memory access (RDMA) over converged Ethernet (RoCE) was an attempt to
adopt modern RDMA features into existing Ethernet installations. We revisit RoCE's design …

We observe that emerging artificial intelligence, high-performance computing, and storage
workloads pose new challenges for large-scale datacenter networking. RDMA over …

Bursts, sudden surges in network utilization, are a significant root cause of packet loss and
high latency in datacenters. Packet deflection, re-routing packets that arrive at a local …

The surge of artificial intelligence, specifically large language models, has led to a rapid
advent towards the development of large-scale machine learning training clusters …

Traffic from distributed training of machine learning (ML) models makes up a large and
growing fraction of the traffic mix in enterprise data centers. While work on distributed ML …

We propose a novel GPU-cluster scheduler for distributed DL (DDL) workloads that enables
proximity based consolidation of GPU resources based on the DDL jobs' sensitivities to the …

Abstract Remote Direct Memory Access (RDMA) is widely deployed in data centers to
improve the performance, efficiency, and reliability of data centers. Priority-based Flow …