Breaking the Computation and Communication Abstraction Barrier in Distributed Machine Learning Workloads

12 May 2021

Papers citing "Breaking the Computation and Communication Abstraction Barrier in Distributed Machine Learning Workloads"

7 / 7 papers shown

Title
MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications Aashaka Shah Abhinav Jangda Yangqiu Song Caio Rocha Changho Hwang ... Peng Cheng Qinghua Zhou Roshan Dathathri Saeed Maleki Ziyue Yang GNN 54 0 0 11 Apr 2025
Importance Sampling via Score-based Generative Models Heasung Kim Taekyun Lee Hyeji Kim Gustavo de Veciana MedIm DiffM 141 0 0 07 Feb 2025
ProTrain: Efficient LLM Training via Memory-Aware Techniques Hanmei Yang Jin Zhou Yao Fu Xiaoqun Wang Ramine Roane Hui Guan Tongping Liu VLM 36 0 0 12 Jun 2024
Optimus-CC: Efficient Large NLP Model Training with 3D Parallelism Aware Communication Compression Jaeyong Song Jinkyu Yim Jaewon Jung Hongsun Jang H. Kim Youngsok Kim Jinho Lee GNN 24 25 0 24 Jan 2023
TAPS: Topology-Aware Intra-Operator Parallelism Strategy Searching Algorithm for Deep Neural Networks Peng Liang Hao Zheng Teng Su Linbo Qiao Dongsheng Li 30 0 0 11 Jan 2023
Optimizing DNN Compilation for Distributed Training with Joint OP and Tensor Fusion Xiaodong Yi Shiwei Zhang Lansong Diao Chuan Wu Zhen Zheng Shiqing Fan Siyu Wang Jun Yang W. Lin 39 4 0 26 Sep 2022
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism M. Shoeybi M. Patwary Raul Puri P. LeGresley Jared Casper Bryan Catanzaro MoE 245 1,826 0 17 Sep 2019