Distributed deep learning (DDL) systems strongly depend on network performance. Current
electronic packet switched (EPS) network architectures and technologies suffer from …

Distributed deep training has become a significant consumer of bandwidth across
datacenter-scale networks. The diverse parallel strategies employed in deep training require …

With the ever-increasing size of training models and datasets, network communication has
emerged as a major bottleneck in distributed deep learning training. To address this …

Data center networks are experiencing unprecedented exponential growth, mostly driven by
the continuous computing demands in machine learning and artificial intelligence …

The increasing size and complexity of deep learning (DL) models have led to the wide
adoption of distributed training methods in datacenters (DCs) and high-performance …

Data parallelism is the dominant method used to train deep learning (DL) model on High-
Performance Computing systems such as large-scale GPU clusters. When training a DL …

Networks are the well-known bottlenecks for distributed deep learning (DDL) jobs. The DDL
jobs require topologies matching their communication patterns as well as high and stable …

We run various distributed machine learning (DML) architectures in a hybrid
optical/electrical DCN and an optical DCN based on Hyper-FleX-LION. Experimental results …

The scaling trends of deep learning models and distributed training workloads are
challenging network capacities in today's datacenters and high-performance computing …

As Deep Learning (DL) models grow larger and more complex, training jobs are
increasingly distributed across multiple Computing Units (CU) such as GPUs and TPUs …