| Beyond data and model parallelism for deep neural networks Z Jia, M Zaharia, A Aiken SysML 19, 2019 | 266 | 2019 |
| Improving integer security for systems with {KINT} X Wang, H Chen, Z Jia, N Zeldovich, MF Kaashoek 10th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2012 | 139 | 2012 |
| TASO: Optimizing Deep Learning Computation with Automatic Generation of Graph Substitutions Z Jia, O Padon, J Thomas, T Warszawski, M Zaharia, A Aiken SOSP'19, 2019 | 125 | 2019 |
| Undefined behavior: what happened to my code? X Wang, H Chen, A Cheung, Z Jia, N Zeldovich, MF Kaashoek Proceedings of the Asia-Pacific Workshop on Systems, 1-7, 2012 | 105 | 2012 |
| Exploring hidden dimensions in parallelizing convolutional neural networks Z Jia, S Lin, CR Qi, A Aiken ICML 18, 2018 | 89 | 2018 |
| Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc Z Jia, S Lin, M Gao, M Zaharia, A Aiken MLSys'20, 2020 | 88 | 2020 |
| A distributed multi-gpu system for fast graph processing Z Jia, Y Kwon, G Shipman, P McCormick, M Erez, A Aiken Proceedings of the VLDB Endowment 11 (3), 297-310, 2017 | 65 | 2017 |
| {SLIK}: Scalable {Low-Latency} Indexes for a {Key-Value} Store A Kejriwal, A Gopalan, A Gupta, Z Jia, S Yang, J Ousterhout 2016 USENIX Annual Technical Conference (USENIX ATC 16), 57-70, 2016 | 49 | 2016 |
| Optimizing DNN Computation With Relaxed Graph Substitutions Z Jia, J Thomas, T Warszawski, M Gao, M Zaharia, A Aiken SysML 2019, 2019 | 44 | 2019 |
| Redundancy-Free Computation for Graph Neural Networks Z Jia, S Lin, R Ying, J You, J Leskovec, A Aiken KDD'20, 2019 | 38* | 2019 |
| Dorylus: Affordable, Scalable, and Accurate {GNN} Training with Distributed {CPU} Servers and Serverless Threads J Thorpe, Y Qiao, J Eyolfson, S Teng, G Hu, Z Jia, J Wei, K Vora, ... 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2021 | 34 | 2021 |
| Exploring hidden dimensions in accelerating convolutional neural networks Z Jia, S Lin, CR Qi, A Aiken International Conference on Machine Learning, 2274-2283, 2018 | 23 | 2018 |
| Ios: Inter-operator scheduler for cnn acceleration Y Ding, L Zhu, Z Jia, G Pekhimenko, S Han Proceedings of Machine Learning and Systems 3, 167-180, 2021 | 15 | 2021 |
| {PET}: Optimizing Tensor Programs with Partially Equivalent Transformations and Automated Corrections H Wang, J Zhai, M Gao, Z Ma, S Tang, L Zheng, Y Li, K Rong, Y Chen, ... 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2021 | 14 | 2021 |
| Automatic and transparent I/O optimization with storage integrated application runtime support N Watkins, Z Jia, G Shipman, C Maltzahn, A Aiken, P McCormick Proceedings of the 10th Parallel Data Storage Workshop, 49-54, 2015 | 10 | 2015 |
| Software-hardware co-design for fast and scalable training of deep learning recommendation models D Mudigere, Y Hao, J Huang, Z Jia, A Tulloch, S Sridharan, X Liu, ... Proceedings of the 49th Annual International Symposium on Computer …, 2022 | 8 | 2022 |
| Scaling Implicit Parallelism via Dynamic Control Replication M Bauer, W Lee, E Slaughter, Z Jia, M Di Renzo, M Papadakis, ... PPoPP, 2021 | 8 | 2021 |
| Beyond data and model parallelism for deep neural networks. CoRR abs/1807.05358 (2018) Z Jia, M Zaharia, A Aiken arXiv preprint arXiv:1807.05358, 1807 | 8 | 1807 |
| TopoOpt: Optimizing the Network Topology for Distributed DNN Training W Wang, M Khazraee, Z Zhong, Z Jia, D Mudigere, Y Zhang, A Kewitsch, ... arXiv preprint arXiv:2202.00433, 2022 | 4 | 2022 |
| Quanto: Optimizing quantum circuits with automatic generation of circuit identities J Pointing, O Padon, Z Jia, H Ma, A Hirth, J Palsberg, A Aiken arXiv preprint arXiv:2111.11387, 2021 | 4 | 2021 |
Alex AikenProfessor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
