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20182022
most citedBigRoots: An Effective Approach for Root-cause Analysis of Stragglers in Big Data System

6 citations · 9 across the 4 of their papers we have counts for

collaborators

6 papers

cs.DC20222 cited

Mimose: An Input-Aware Checkpointing Planner for Efficient Training on GPU

Jianjin Liao, Mingzhen Li, Qingxiao Sun +8

Larger deep learning models usually lead to higher model quality with an ever-increasing GPU memory footprint. Although tensor checkpointing techniques have been proposed to enable…

cs.CR20191 cited

Privacy for Rescue: A New Testimony Why Privacy is Vulnerable In Deep Models

Ruiyuan Gao, Ming Dun, Hailong Yang +2

The huge computation demand of deep learning models and limited computation resources on the edge devices calls for the cooperation between edge device and cloud service by splitti…

cs.DC2019

Massively Scaling Seismic Processing on Sunway TaihuLight Supercomputer

Yongmin Hu, Hailong Yang, Zhongzhi Luan +1

Common Midpoint (CMP) and Common Reflection Surface (CRS) are widely used methods for improving the signal-to-noise ratio in the field of seismic processing. These methods are comp…

cs.PF2019

Redundant Loads: A Software Inefficiency Indicator

Pengfei Su, Shasha Wen, Hailong Yang +2

Modern software packages have become increasingly complex with millions of lines of code and references to many external libraries. Redundant operations are a common performance li…

cs.LG2018

Generative Model for Heterogeneous Inference

Honggang Zhou, Yunchun Li, Hailong Yang +2

Generative models (GMs) such as Generative Adversary Network (GAN) and Variational Auto-Encoder (VAE) have thrived these years and achieved high quality results in generating new s…

cs.DC20186 cited

BigRoots: An Effective Approach for Root-cause Analysis of Stragglers in Big Data System

Honggang Zhou, Yunchun Li, Hailong Yang +2

Stragglers are commonly believed to have a great impact on the performance of big data system. However, the reason to cause straggler is complicated. Previous works mostly focus on…