LIBRA: Enabling Workload-aware Multi-dimensional Network Topology
Optimization for Distributed Training of Large AI Models

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LIBRA: Enabling Workload-aware Multi-dimensional Network Topology Optimization for Distributed Training of Large AI Models

24 September 2021

Sudarshan Srinivasan

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Papers citing "LIBRA: Enabling Workload-aware Multi-dimensional Network Topology Optimization for Distributed Training of Large AI Models"

16 / 16 papers shown

Title
TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings N. Jouppi George Kurian Sheng Li Peter C. Ma R. Nagarajan ... Brian Towles C. Young Xiaoping Zhou Zongwei Zhou David A. Patterson BDL VLM 138 366 0 04 Apr 2023
ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale William Won Taekyung Heo Saeed Rashidi Srinivas Sridharan Sudarshan Srinivasan T. Krishna 54 51 0 24 Mar 2023
TopoOpt: Co-optimizing Network Topology and Parallelization Strategy for Distributed Training Jobs Weiyang Wang Moein Khazraee Zhizhen Zhong M. Ghobadi Zhihao Jia Dheevatsa Mudigere Ying Zhang A. Kewitsch 78 92 0 01 Feb 2022
DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale Samyam Rajbhandari Conglong Li Z. Yao Minjia Zhang Reza Yazdani Aminabadi A. A. Awan Jeff Rasley Yuxiong He 112 302 0 14 Jan 2022
Themis: A Network Bandwidth-Aware Collective Scheduling Policy for Distributed Training of DL Models Saeed Rashidi William Won Sudarshan Srinivasan Srinivas Sridharan T. Krishna GNN 72 34 0 09 Oct 2021
Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models Dheevatsa Mudigere Y. Hao Jianyu Huang Zhihao Jia Andrew Tulloch ... Ajit Mathews Lin Qiao M. Smelyanskiy Bill Jia Vijay Rao 81 153 0 12 Apr 2021
Enabling Compute-Communication Overlap in Distributed Deep Learning Training Platforms Saeed Rashidi Matthew Denton Srinivas Sridharan Sudarshan Srinivasan Amoghavarsha Suresh Jade Nie T. Krishna 80 49 0 30 Jun 2020
Language Models are Few-Shot Learners Tom B. Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan ... Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever Dario Amodei BDL 880 42,379 0 28 May 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 611 4,905 0 23 Jan 2020
A Survey on Distributed Machine Learning Joost Verbraeken Matthijs Wolting Jonathan Katzy Jeroen Kloppenburg Tim Verbelen Jan S. Rellermeyer OOD 102 713 0 20 Dec 2019
Blink: Fast and Generic Collectives for Distributed ML Guanhua Wang Shivaram Venkataraman Amar Phanishayee J. Thelin Nikhil R. Devanur Ion Stoica VLM 54 140 0 11 Oct 2019
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism Mohammad Shoeybi M. Patwary Raul Puri P. LeGresley Jared Casper Bryan Catanzaro MoE 336 1,917 0 17 Sep 2019
Deep Learning Recommendation Model for Personalization and Recommendation Systems Maxim Naumov Dheevatsa Mudigere Hao-Jun Michael Shi Jianyu Huang Narayanan Sundaraman ... Wenlin Chen Vijay Rao Bill Jia Liang Xiong M. Smelyanskiy 91 738 0 31 May 2019
Attention Is All You Need Ashish Vaswani Noam M. Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan Gomez Lukasz Kaiser Illia Polosukhin 3DV 787 132,454 0 12 Jun 2017
In-Datacenter Performance Analysis of a Tensor Processing Unit N. Jouppi C. Young Nishant Patil David Patterson Gaurav Agrawal ... Vijay Vasudevan Richard Walter Walter Wang Eric Wilcox Doe Hyun Yoon 237 4,644 0 16 Apr 2017
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 194,426 0 10 Dec 2015

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