SLAQ: Quality-Driven Scheduling for Distributed Machine Learning

13 February 2018

Papers citing "SLAQ: Quality-Driven Scheduling for Distributed Machine Learning"

9 / 9 papers shown

Title
EasyScale: Accuracy-consistent Elastic Training for Deep Learning Mingzhen Li Wencong Xiao Biao Sun Hanyu Zhao Hailong Yang ... Xianyan Jia Yi Liu Yong Li Wei Lin D. Qian 22 7 0 30 Aug 2022
MLLess: Achieving Cost Efficiency in Serverless Machine Learning Training Pablo Gimeno Sarroca Marc Sánchez Artigas 26 14 0 12 Jun 2022
Online Evolutionary Batch Size Orchestration for Scheduling Deep Learning Workloads in GPU Clusters Chen Sun Shenggui Li Jinyue Wang Jun Yu 54 47 0 08 Aug 2021
Learning-NUM: Network Utility Maximization with Unknown Utility Functions and Queueing Delay Xinzhe Fu E. Modiano 16 18 0 16 Dec 2020
Speculative Container Scheduling for Deep Learning Applications in a Kubernetes Cluster Ying Mao Yuqi Fu Wenjia Zheng Long Cheng Qingzhi Liu Dingwen Tao 13 29 0 21 Oct 2020
VirtualFlow: Decoupling Deep Learning Models from the Underlying Hardware Andrew Or Haoyu Zhang M. Freedman 17 9 0 20 Sep 2020
DL2: A Deep Learning-driven Scheduler for Deep Learning Clusters Size Zheng Yixin Bao Yangrui Chen Chuan Wu Chen Meng Wei Lin 21 79 0 13 Sep 2019
Characterizing Application Scheduling on Edge, Fog and Cloud Computing Resources Prateeksha Varshney Yogesh L. Simmhan 31 57 0 23 Apr 2019
Stratum: A Serverless Framework for Lifecycle Management of Machine Learning based Data Analytics Tasks Anirban Bhattacharjee Yogesh D. Barve S. Khare Shunxing Bao A. Gokhale Thomas Damiano 26 28 0 03 Apr 2019