CROSSBOW: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers

8 January 2019

Papers citing "CROSSBOW: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers"

9 / 9 papers shown

Title
GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models Hanjing Wang Man-Kit Sit Cong He Ying Wen Weinan Zhang Jun Wang Yaodong Yang Luo Mai OffRL VLM 40 1 0 08 Oct 2023
The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey Jana Vatter R. Mayer Hans-Arno Jacobsen GNN AI4TS AI4CE 48 23 0 23 May 2023
Understand Data Preprocessing for Effective End-to-End Training of Deep Neural Networks Ping Gong Yuxin Ma Cheng-rong Li Xiaosong Ma S. Noh 13 2 0 18 Apr 2023
GPU-enabled Function-as-a-Service for Machine Learning Inference Ming Zhao Kritshekhar Jha Sungho Hong 35 7 0 09 Mar 2023
An Analysis of Collocation on GPUs for Deep Learning Training Ties Robroek Ehsan Yousefzadeh-Asl-Miandoab Pınar Tözün 20 9 0 13 Sep 2022
Adaptive Elastic Training for Sparse Deep Learning on Heterogeneous Multi-GPU Servers Yujing Ma Florin Rusu Kesheng Wu A. Sim 46 3 0 13 Oct 2021
Optimizing Multi-GPU Parallelization Strategies for Deep Learning Training Saptadeep Pal Eiman Ebrahimi A. Zulfiqar Yaosheng Fu Victor Zhang Szymon Migacz D. Nellans Puneet Gupta 34 55 0 30 Jul 2019
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 308 2,890 0 15 Sep 2016
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 183 1,185 0 30 Nov 2014