Gradient compression is a promising approach to alleviating the communication bottleneck
in data parallel deep neural network (DNN) training by significantly reducing the data …

For distributed training (DT) based on the parameter servers (PS) architecture, the
communication overhead is huge in the network for servers synchronizing parameters. In the …

With the increasing complexity of computational tasks faced by artificial intelligence
technology, the scale of machine learning models continues to expand, and the data volume …

AllReduce is a collective communication pattern commonly used in Distributed Deep
Learning (DDL) and High Performance Computing (HPC). Sparse AllReduce, which …

Overlapping gradient communication with backward computation is a popular technique to
reduce communication cost in the widely adopted data parallel S-SGD training. However …

Distributed deep learning training usually adopts All-Reduce as the synchronization
mechanism for data parallel algorithms due to its high performance in homogeneous …

Interpreting network telemetry data is difficult. Size and volume are network assets.
Production rises. ML predicts traffic trends to help decision-making. Classification and …

Deep Neural Network (DNN) training on a large scale is extremely time-consuming and
computationally intensive, which is accelerated by distributed training. In recent years …

In distributed deep neural network using remote GPU nodes, communication occurs
iteratively between remote nodes for gradient aggregation. This communication latency …

Numerous distributed applications, including machine learning and big data analysis, have
suffered performance degradation from network bottleneck. To solve this problem …