| Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent X Lian, C Zhang, H Zhang, CJ Hsieh, W Zhang, J Liu arXiv preprint arXiv:1705.09056, 2017 | 367 | 2017 |
| Automated atomicity-violation fixing G Jin, L Song, W Zhang, S Lu, B Liblit PLDI 2011, 389-400, 2011 | 249 | 2011 |
| Staleness-aware Async-SGD for Distributed Deep Learning W Zhang, S Gupta, X Lian, J Liu IJCAI 2016, 2016 | 178 | 2016 |
| Automated concurrency-bug fixing G Jin, W Zhang, D Deng, B Liblit, S Lu OSDI 2012, 221-236, 2012 | 177 | 2012 |
| Asynchronous decentralized parallel stochastic gradient descent X Lian*, W Zhang*, C Zhang, J Liu ICML 2018, 2017 | 159 | 2017 |
| ConSeq: detecting concurrency bugs through sequential errors W Zhang, J Lim, R Olichandran, J Scherpelz, G Jin, S Lu, T Reps ASPLOS 2011, 251-264, 2011 | 137 | 2011 |
| ConMem: detecting severe concurrency bugs through an effect-oriented approach W Zhang, C Sun, S Lu ASPLOS'10 45 (3), 179-192, 2010 | 129 | 2010 |
| Model accuracy and runtime tradeoff in distributed deep learning: A systematic study S Gupta*, W Zhang*, F Wang 2016 IEEE 16th International Conference on Data Mining (ICDM), 171-180, 2016 | 126 | 2016 |
| Adacomp: Adaptive residual gradient compression for data-parallel distributed training CY Chen, J Choi, D Brand, A Agrawal, W Zhang, K Gopalakrishnan Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 83 | 2018 |
| ConAir: Featherweight concurrency bug recovery via single-threaded idempotent execution W Zhang, M De Kruijf, A Li, S Lu, K Sankaralingam Proceedings of the eighteenth international conference on Architectural …, 2013 | 48 | 2013 |
| GLB: lifeline-based global load balancing library in x10 W Zhang, O Tardieu, D Grove, B Herta, T Kamada, V Saraswat, ... Proceedings of the first workshop on Parallel programming for analytics …, 2014 | 47 | 2014 |
| Hybrid 8-bit floating point (HFP8) training and inference for deep neural networks X Sun, J Choi, CY Chen, N Wang, S Venkataramani, VV Srinivasan, X Cui, ... Advances in neural information processing systems 32, 4900-4909, 2019 | 35 | 2019 |
| Model accuracy and runtime tradeoff in distributed deep learning S Gupta, W Zhang, J Milthorpe arXiv preprint arXiv:1509.04210, 2015 | 35 | 2015 |
| Efficient Concurrency-Bug Detection Across Inputs D Deng, W Zhang, L Shan Object-Oriented Programming, Systems, Languages & Applications, 2013 | 32 | 2013 |
| Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks X Cui, Z Wei, Z Tuske, M Picheny NIPS'2018, 2018 | 30 | 2018 |
| Conmem: Detecting crash-triggering concurrency bugs through an effect-oriented approach W Zhang, C Sun, J Lim, S Lu, T Reps ACM Transactions on Software Engineering and Methodology (TOSEM) 22 (2), 1-33, 2013 | 18 | 2013 |
| Towards better understanding of adaptive gradient algorithms in generative adversarial nets M Liu, Y Mroueh, J Ross, W Zhang, X Cui, P Das, T Yang arXiv preprint arXiv:1912.11940, 2019 | 17 | 2019 |
| Distributed Deep Learning Strategies For Automatic Speech Regcontion W Zhang, X Cui, U Finkler, B Kingsbury, G Saon, D Kung, M Picheny ICASSP'19, 2019 | 15 | 2019 |
| Fixing, preventing, and recovering from concurrency bugs DD Deng, GL Jin, M de Kruijf, A Li, B Liblit, S Lu, SX Qi, JL Ren, ... Science China Information Sciences 58 (5), 1-18, 2015 | 14 | 2015 |
| Gadei: On scale-up training as a service for deep learning W Zhang, M Feng, Y Zheng, Y Ren, Y Wang, J Liu, P Liu, B Xiang, ... 2017 IEEE International Conference on Data Mining (ICDM), 1195-1200, 2017 | 12 | 2017 |
Shan LuProfessor of Computer Science, University of ChicagoVerified email at uchicago.edu
