王林楠 (Linnan Wang)

Office 351, CIT
Department of Computer Science
Brown University
Providence, RI 02906
Email: wangnan318@gmail.com

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Brief Bio:

I'm a Ph.D. student at the CS department of Brown University, advised by Prof.Rodrigo Fonseca. Before Brown, I was a OMSCS student at Gatech while being a full time software developer at Dow Jones . I acquired my bachelor degree from University of Electronic Science and Technology of China (UESTC) at the beautiful Qing Shui He campus in 2011.

My research interests are Supercomputing and Neural Networks. Particularly I'm really into scaling the coolest ML algorithms on top500 supercomputers or multiGPU shared memory machines. It also makes me very excited in inventing new ML algorithms to make impossible possible.

I work closely with Wei Wu, George Bosilca, Jack Dongarra, Yang Yi in Supercomputing.

in Submission:

Wang, Linnan, Wei Wu, Yiyang Zhao, Hang Liu, George Bosilca, Jack Dongarra, Maurice Herlihy, Rodrigo Fonseca
SuperNeurons: Gradient Compression in the Distributed Training of Deep Neural Networks

Publications:

2018

Luo, Xi , Wei Wu, George Bosilca, Thananon Patinyasakdikul, Linnan Wang, Jack Dongarra
ADAPT: An Event-based Adaptive Collective Communication Framework
To Appear 27th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC2018)

Li, Ang, Weifeng Liu, Linnan Wang, Kevin Barker, Shuaiwen Leon Song
Warp-Consolidation: A Novel Execution Model for Modern GPUs
To Appear Proceedings of the 2016 International Conference on Supercomputing (ICS2018)

Carsten Binnig, et al.
Towards Interactive Curation & Automatic Tuning of ML Pipelines
SysML Conference (SysML2018)
Paper 

Ye, Jinmian, Linnan Wang, Guangxi Li, Di Chen, Shandian Zhe, Xinqi Chu, Zenglin Xu
Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition
To Appear Proceedings of 31th IEEE Conference on Computer Vision and Pattern Recognition (CVPR2018)
Paper 

Wang, Linnan, Jinmian Ye, Yiyang Zhao, Wei Wu, Ang Li, Shuaiwen Leon Song, Zenglin Xu, Tim Kraska
SuperNeurons:Dynamic GPU Memory Management for Training Deep Nonlinear Neural Networks
In Proceedings of the 23nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP2018)
Paper ·  Talk ·  Presentation ·  Code

2017

Wang, Linnan, Yi Yang, Renqiang Min, and Srimat Chakradhar
Accelerating Deep Neural Network Training with Inconsistent Stochastic Gradient Descent
Neural Networks (2017)
Paper ·  Patent

Zhao, Yiyang, Linnan Wang, Wei Wu, George Bosilca, Richard Vuduc, Jinmian Ye, Wenqi Tang, and Zenglin Xu.
Efficient Communications in Training Large Scale Neural Networks
In Proceedings of the 25th ACM international conference on Multimedia (MM2017)
Paper

Li, Guangxi, Zenglin Xu, Linnan Wang, Jinmian Ye, Irwin King, and Michael Lyu
Simple and Efficient Parallelization for Probabilistic Temporal Tensor Factorization
In 2017 International Joint Conference on Neural Networks (IJCNN2017)
Paper

2016

Wang, Linnan, Wei Wu, Zenglin Xu, Jianxiong Xiao, and Yi Yang
BLASX: A High Performance Level-3 BLAS Library for Heterogeneous MultiGPU Computing
In Proceedings of the 2016 International Conference on Supercomputing (ICS2016)
Paper ·  SC Poster ·  Code ·  Presentation

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