99 citations · 223 across the 15 of their papers we have counts for
9 papers · 1 filter
Energy-aware Task Scheduling with Deadline Constraint in DVFS-enabled Heterogeneous Clusters
Xinxin Mei, Qiang Wang, Xiaowen Chu +3
Energy conservation of large data centers for high-performance computing workloads, such as deep learning with big data, is of critical significance, where cutting down a few perce…
Towards Scalable Distributed Training of Deep Learning on Public Cloud Clusters
Shaohuai Shi, Xianhao Zhou, Shutao Song +21
Distributed training techniques have been widely deployed in large-scale deep neural networks (DNNs) training on dense-GPU clusters. However, on public cloud clusters, due to the m…
Performance Characterization and Bottleneck Analysis of Hyperledger Fabric
Canhui Wang, Xiaowen Chu
Hyperledger Fabric is a popular open-source project for deploying permissioned blockchains. Many performance characteristics of the latest Hyperledger Fabric, such as performance c…
Efficient Sparse-Dense Matrix-Matrix Multiplication on GPUs Using the Customized Sparse Storage Format
Shaohuai Shi, Qiang Wang, Xiaowen Chu
Multiplication of a sparse matrix to a dense matrix (SpDM) is widely used in many areas like scientific computing and machine learning. However, existing works under-look the perfo…
A Quantitative Survey of Communication Optimizations in Distributed Deep Learning
Shaohuai Shi, Zhenheng Tang, Xiaowen Chu +3
Nowadays, large and complex deep learning (DL) models are increasingly trained in a distributed manner across multiple worker machines, in which extensive communications between wo…
Communication Contention Aware Scheduling of Multiple Deep Learning Training Jobs
Qiang Wang, Shaohuai Shi, Canhui Wang +1
Distributed Deep Learning (DDL) has rapidly grown its popularity since it helps boost the training performance on high-performance GPU clusters. Efficient job scheduling is indispe…