329 citations · 536 across the 6 of their papers we have counts for
6 papers
Proteus: Simulating the Performance of Distributed DNN Training
Jiangfei Duan, Xiuhong Li, Ping Xu +4
DNN models are becoming increasingly larger to achieve unprecedented accuracy, and the accompanying increased computation and memory requirements necessitate the employment of mass…
A Simulation Platform for Multi-tenant Machine Learning Services on Thousands of GPUs
Ruofan Liang, Bingsheng He, Shengen Yan +1
Multi-tenant machine learning services have become emerging data-intensive workloads in data centers with heavy usage of GPU resources. Due to the large scale, many tuning paramete…
Characterization and Prediction of Deep Learning Workloads in Large-Scale GPU Datacenters
Qinghao Hu, Peng Sun, Shengen Yan +2
Modern GPU datacenters are critical for delivering Deep Learning (DL) models and services in both the research community and industry. When operating a datacenter, optimization of…
Optimizing Network Performance for Distributed DNN Training on GPU Clusters: ImageNet/AlexNet Training in 1.5 Minutes
Peng Sun, Wansen Feng, Ruobing Han +2
It is important to scale out deep neural network (DNN) training for reducing model training time. The high communication overhead is one of the major performance bottlenecks for di…
Towards Distributed Machine Learning in Shared Clusters: A Dynamically-Partitioned Approach
Peng Sun, Yonggang Wen, Ta Nguyen Binh Duong +1
Many cluster management systems (CMSs) have been proposed to share a single cluster with multiple distributed computing systems. However, none of the existing approaches can handle…
Deep Image: Scaling up Image Recognition
Ren Wu, Shengen Yan, Yi Shan +2
We present a state-of-the-art image recognition system, Deep Image, developed using end-to-end deep learning. The key components are a custom-built supercomputer dedicated to deep…