Analyzing and Mitigating Data Stalls in DNN Training

14 July 2020

Papers citing "Analyzing and Mitigating Data Stalls in DNN Training"

8 / 8 papers shown

Title
Analyzing I/O Performance of a Hierarchical HPC Storage System for Distributed Deep Learning Takaaki Fukai Kento Sato Takahiro Hirofuchi 22 2 0 04 Jan 2023
A Study on the Intersection of GPU Utilization and CNN Inference J. Kosaian Amar Phanishayee 13 3 0 15 Dec 2022
tf.data service: A Case for Disaggregating ML Input Data Processing Andrew Audibert Yangrui Chen D. Graur Ana Klimovic Jiří Šimša C. A. Thekkath 42 16 0 26 Oct 2022
L3: Accelerator-Friendly Lossless Image Format for High-Resolution, High-Throughput DNN Training Jonghyun Bae W. Baek Tae Jun Ham Jae W. Lee 15 1 0 18 Aug 2022
Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines Alexander Isenko R. Mayer Jeffrey Jedele Hans-Arno Jacobsen 19 23 0 17 Feb 2022
Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines Michael Kuchnik Ana Klimovic Jiří Šimša Virginia Smith George Amvrosiadis 50 30 0 07 Nov 2021
Understanding Data Storage and Ingestion for Large-Scale Deep Recommendation Model Training Mark Zhao Niket Agarwal Aarti Basant B. Gedik Satadru Pan ... Kevin Wilfong Harsha Rastogi Carole-Jean Wu Christos Kozyrakis Parikshit Pol GNN 15 70 0 20 Aug 2021
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation Yonghui Wu M. Schuster Z. Chen Quoc V. Le Mohammad Norouzi ... Alex Rudnick Oriol Vinyals G. Corrado Macduff Hughes J. Dean AIMat 716 6,743 0 26 Sep 2016