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 …

Deep Learning (DL) training platforms are built by interconnecting multiple DL accelerators
(eg, GPU/TPU) via fast, customized interconnects with 100s of gigabytes (GBs) of bandwidth …

Distributed training is a solution to reduce DNN training time by splitting the task across
multiple NPUs (eg, GPU/TPU). However, distributed training adds communication overhead …

Ahstract-RDMA over Converged Ethernet (RoCE) has gained significant attraction for
datacenter networks due to its compatibility with conventional Ethernet-based fabric …

We aim to resolve this problem by introducing a comprehensive distributed deep learning
(DDL) profiler, which can determine the various execution" stalls" that DDL suffers from while …

With the development of Deep Learning (DL), Deep Neural Network (DNN) models have
become more complex. At the same time, the development of the Internet makes it easy to …

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 …

Distributed Deep Neural Network (DNN) training is a technique to reduce the training
overhead by distributing the training tasks into multiple accelerators, according to a …

With the development of deep learning, DNN models have become more complex. Large-
scale model parameters enhance the level of AI by improving the accuracy of DNN models …

With the continuous development of emerging technologies such as big data, network
application scenarios are becoming more abundant, which makes the network traffic grow …