Wencong Xiao
Wencong Xiao
Alibaba Group
Verified email at - Homepage
Cited by
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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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
All You Need to Know about Scheduling Deep Learning Jobs
W Xiao
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