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3D Gaussian Splatting (3DGS) is increasingly popular for 3D reconstruction due to its superior visual quality and rendering speed.
Accurate, large minibatch sgd: Training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
Earlier work this paper cites.
On large-batch training for deep learning: Generalization gap and sharp minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 2017
Earlier work this paper cites.
Tanks and temples: Benchmarking large-scale scene reconstruction
Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun · 2017
Earlier work this paper cites.
Large batch training of convolutional networks with layer-wise adaptive rate scaling, 2018
Boris Ginsburg, Igor Gitman, and Yang You · 2018
Earlier work this paper cites.
Deep blending for free-viewpoint image-based rendering
Peter Hedman, Julien Philip, True Price, Jan-Michael Frahm, George Drettakis, and Gabriel Brostow · 2018
Earlier work this paper cites.
Gpipe: Efficient training of giant neural networks using pipeline parallelism
Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Dehao Chen, Mia Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V Le, Yonghui Wu, and zhifeng Chen · 2019
Earlier work this paper cites.
Pipedream: generalized pipeline parallelism for dnn training
Deepak Narayanan, Aaron Harlap, Amar Phanishayee, Vivek Seshadri, Nikhil R Devanur, Gregory R Ganger, Phillip B Gibbons, and Matei Zaharia · 2019
Earlier work this paper cites.
Supporting very large models using automatic dataflow graph partitioning
Minjie Wang, Chien-chin Huang, and Jinyang Li · 2019
Earlier work this paper cites.
Pytorch distributed: Experiences on accelerating data parallel training
Shen Li, Yanli Zhao, Rohan Varma, Omkar Salpekar, Pieter Noordhuis, Teng Li, Adam Paszke, Jeff Smith, Brian Vaughan, Pritam Damania, and Soumith Chintala · 2020
Earlier work this paper cites.
Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng · 2020
Earlier work this paper cites.
Zero: Memory optimizations toward training trillion parameter models
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He · 2020
Earlier work this paper cites.
Megatron-lm: Training multi-billion parameter language models using model parallelism
Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro · 2020
Cited alongside, same era.
Large batch optimization for deep learning: Training bert in 76 minutes
Yang You, Jing Li, Sashank Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, and Cho-Jui Hsieh · 2020
Cited alongside, same era.
Efficient large-scale language model training on gpu clusters using megatron-lm
Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Anand Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, Amar Phanishayee, and Matei Zaharia · 2021
Cited alongside, same era.
Pollux: Co-adaptive cluster scheduling for goodput-optimized deep learning
Aurick Qiao, Sang Keun Choe, Suhas Jayaram Subramanya, Willie Neiswanger, Qirong Ho, Hao Zhang, Gregory R. Ganger, and Eric P. Xing · 2021
Cited alongside, same era.
Gspmd: General and scalable parallelization for ml computation graphs
How to scale your EMA
Dan Busbridge, Jason Ramapuram, Pierre Ablin, Tatiana Likhomanenko, Eeshan Gunesh Dhekane, Xavier Suau, and Russell Webb · 2023
Later among the works it cites.
3d gaussian splatting for real-time radiance field rendering
Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis · 2023
Later among the works it cites.
Matrixcity: A large-scale city dataset for city-scale neural rendering and beyond
Yixuan Li, Lihan Jiang, Linning Xu, Yuanbo Xiangli, Zhenzhi Wang, Dahua Lin, and Bo Dai · 2023
Later among the works it cites.
Pytorch fsdp: Experiences on scaling fully sharded data parallel, 2023
Yanli Zhao, Andrew Gu, Rohan Varma, Liang Luo, Chien-Chin Huang, Min Xu, Less Wright, Hamid Shojanazeri, Myle Ott, Sam Shleifer, Alban Desmaison, Can Balioglu, Pritam Damania, Bernard Nguyen, Geeta Chauhan, Yuchen Hao, Ajit Mathews, and Shen Li · 2023
Later among the works it cites.
Yu Chen and Gim Hee Lee · 2024
Closest in time.
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Yuanzhong Xu, HyoukJoong Lee, Dehao Chen, Blake Hechtman, Yanping Huang, Rahul Joshi, Maxim Krikun, Dmitry Lepikhin, Andy Ly, Marcello Maggioni, Ruoming Pang, Noam Shazeer, Shibo Wang, Tao Wang, Yonghui Wu, and Zhifeng Chen · 2021
Cited alongside, same era.
Mip-nerf 360: Unbounded anti-aliased neural radiance fields
Jonathan Barron, Ben Mildenhall, Dor Verbin, Pratul Srinivasan, and Peter Hedman · 2022
Cited alongside, same era.
Learning rates as a function of batch size: A random matrix theory approach to neural network training
Diego Granziol, Stefan Zohren, and Stephen Roberts · 2022
Cited alongside, same era.
On the SDEs and scaling rules for adaptive gradient algorithms
Sadhika Malladi, Kaifeng Lyu, Abhishek Panigrahi, and Sanjeev Arora · 2022
Cited alongside, same era.
Mega-nerf: Scalable construction of large-scale nerfs for virtual fly-throughs
Haithem Turki, Deva Ramanan, and Mahadev Satyanarayanan · 2022
Cited alongside, same era.
Bungeenerf: Progressive neural radiance field for extreme multi-scale scene rendering
Xiangli Yuanbo, Xu Linning, Pan Xingang, Zhao Nanxuan, Rao Anyi, Theobalt Christian, Dai Bo, and Lin Dahua · 2022
Cited alongside, same era.
Alpa: Automating inter-and intra-operator parallelism for distributed deep learning
Lianmin Zheng, Zhuohan Li, Hao Zhang, Yonghao Zhuang, Zhifeng Chen, Yanping Huang, Yida Wang, Yuanzhong Xu, Danyang Zhuo, Eric P Xing, et al · 2022
Cited alongside, same era.
Retinags: Scalable training for dense scene rendering with billion-scale 3d gaussians, 2024a
Bingling Li, Shengyi Chen, Luchao Wang, Kaimin Liao, Sijie Yan, and Yuanjun Xiong
Cited in the paper.
A hierarchical 3d gaussian representation for real-time rendering of very large datasets
Bernhard Kerbl, Andreas Meuleman, Georgios Kopanas, Michael Wimmer, Alexandre Lanvin, and George Drettakis · 2024
Closest in time.
Vastgaussian: Vast 3d gaussians for large scene reconstruction
Jiaqi Lin, Zhihao Li, Xiao Tang, Jianzhuang Liu, Shiyong Liu, Jiayue Liu, Yangdi Lu, Xiaofei Wu, Songcen Xu, Youliang Yan, and Wenming Yang · 2024
Closest in time.
Citygaussian: Real-time high-quality large-scale scene rendering with gaussians
Yang Liu, He Guan, Chuanchen Luo, Lue Fan, Junran Peng, and Zhaoxiang Zhang · 2024
Closest in time.
Scaffold-gs: Structured 3d gaussians for view-adaptive rendering
Tao Lu, Mulin Yu, Linning Xu, Yuanbo Xiangli, Limin Wang, Dahua Lin, and Bo Dai · 2024
Closest in time.
Perlmutter architecture
NERSC · 2024
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Octree-gs: Towards consistent real-time rendering with lod-structured 3d gaussians
Kerui Ren, Lihan Jiang, Tao Lu, Mulin Yu, Linning Xu, Zhangkai Ni, and Bo Dai · 2024
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