| Gandiva: Introspective cluster scheduling for deep learning W Xiao, R Bhardwaj, R Ramjee, M Sivathanu, N Kwatra, Z Han, P Patel, ... 13th {USENIX} Symposium on Operating Systems Design and Implementation …, 2018 | 330 | 2018 |
| Kv-direct: High-performance in-memory key-value store with programmable nic B Li, Z Ruan, W Xiao, Y Lu, Y Xiong, A Putnam, E Chen, L Zhang Proceedings of the 26th Symposium on Operating Systems Principles, 137-152, 2017 | 225 | 2017 |
| Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads. M Jeon, S Venkataraman, A Phanishayee, J Qian, W Xiao, F Yang USENIX Annual Technical Conference, 947-960, 2019 | 205 | 2019 |
| GraM: scaling graph computation to the trillions M Wu, F Yang, J Xue, W Xiao, Y Miao, L Wei, H Lin, Y Dai, L Zhou Proceedings of the Sixth ACM Symposium on Cloud Computing, 408-421, 2015 | 151 | 2015 |
| Efficient and effective sparse LSTM on FPGA with bank-balanced sparsity S Cao, C Zhang, Z Yao, W Xiao, L Nie, D Zhan, Y Liu, M Wu, L Zhang Proceedings of the 2019 ACM/SIGDA International Symposium on Field …, 2019 | 150 | 2019 |
| Balanced sparsity for efficient dnn inference on gpu Z Yao, S Cao, W Xiao, C Zhang, L Nie Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 5676-5683, 2019 | 78 | 2019 |
| Tux2: Distributed Graph Computation for Machine Learning. W Xiao, J Xue, Y Miao, Z Li, C Chen, M Wu, W Li, L Zhou NSDI, 669-682, 2017 | 68 | 2017 |
| AntMan: Dynamic Scaling on GPU Clusters for Deep Learning. W Xiao, S Ren, Y Li, Y Zhang, P Hou, Z Li, Y Feng, W Lin, Y Jia OSDI, 533-548, 2020 | 66 | 2020 |
| Multi-tenant GPU Clusters for Deep Learning Workloads: Analysis and Implications M Jeon, S Venkataraman, A Phanishayee, J Qian, W Xiao, F Yang MSR-TR-2018-13, 2018 | 59 | 2018 |
| Seernet: Predicting convolutional neural network feature-map sparsity through low-bit quantization S Cao, L Ma, W Xiao, C Zhang, Y Liu, L Zhang, L Nie, Z Yang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 58 | 2019 |
| An empirical study on program failures of deep learning jobs R Zhang, W Xiao, H Zhang, Y Liu, H Lin, M Yang Proceedings of the ACM/IEEE 42nd International Conference on Software …, 2020 | 47 | 2020 |
| MLaaS in the wild: Workload analysis and scheduling in Large-Scale heterogeneous GPU clusters Q Weng, W Xiao, Y Yu, W Wang, C Wang, J He, Y Li, L Zhang, W Lin, ... 19th USENIX Symposium on Networked Systems Design and Implementation (NSDI …, 2022 | 38 | 2022 |
| Memory efficient loss recovery for hardware-based transport in datacenter Y Lu, G Chen, Z Ruan, W Xiao, B Li, J Zhang, Y Xiong, P Cheng, E Chen Proceedings of the First Asia-Pacific Workshop on Networking, 22-28, 2017 | 19 | 2017 |
| Zico: Efficient GPU Memory Sharing for Concurrent DNN Training. G Lim, J Ahn, W Xiao, Y Kwon, M Jeon USENIX Annual Technical Conference, 161-175, 2021 | 9 | 2021 |
| Distributed graph computation meets machine learning W Xiao, J Xue, Y Miao, Z Li, C Chen, M Wu, W Li, L Zhou IEEE Transactions on Parallel and Distributed Systems 31 (7), 1588-1604, 2020 | 9 | 2020 |
| Scheduling CPU for GPU-based deep learning jobs W Xiao, Z Han, H Zhao, X Peng, Q Zhang, F Yang, L Zhou Proceedings of the ACM Symposium on Cloud Computing, 503-503, 2018 | 9 | 2018 |
| BeamRaster: a practical fast massive MU-MIMO system with pre-computed precoders M Meng, W Xiao, T He, Y Tao, K Tan, J Zhang, W Wang IEEE Transactions on Mobile Computing 18 (5), 1014-1027, 2018 | 8 | 2018 |
| Whale: Scaling deep learning model training to the trillions X Jia, L Jiang, A Wang, J Zhang, X Li, W Xiao, Y Li, Z Zheng, X Liu, W Lin arXiv preprint arXiv:2011.09208, 2020 | 4 | 2020 |
| Aligraph: An industrial graph neural network platform K Zhao, W Xiao, B Ai, W Shen, X Zhang, Y Li, W Lin Proc. of SOSP Workshop on AI Systems, 2019 | 3 | 2019 |
| All You Need to Know about Scheduling Deep Learning Jobs W Xiao | 1 | 2017 |
Fan YangMicrosoft ResearchVerified email at microsoft.com
