DAPPLE: A pipelined data parallel approach for training large models S Fan, Y Rong, C Meng, Z Cao, S Wang, Z Zheng, C Wu, G Long, J Yang, ... Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of …, 2021 | 155 | 2021 |
Understanding and bridging the gaps in current GNN performance optimizations K Huang, J Zhai, Z Zheng, Y Yi, X Shen Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of …, 2021 | 69 | 2021 |
Refactoring and optimizing the community atmosphere model (CAM) on the sunway taihulight supercomputer H Fu, J Liao, W Xue, L Wang, D Chen, L Gu, J Xu, N Ding, X Wang, C He, ... SC'16: Proceedings of the International Conference for High Performance …, 2016 | 41 | 2016 |
AStitch: enabling a new multi-dimensional optimization space for memory-intensive ML training and inference on modern SIMT architectures Z Zheng, X Yang, P Zhao, G Long, K Zhu, F Zhu, W Zhao, X Liu, J Yang, ... Proceedings of the 27th ACM International Conference on Architectural …, 2022 | 38 | 2022 |
Versapipe: a versatile programming framework for pipelined computing on GPU Z Zheng, C Oh, J Zhai, X Shen, Y Yi, W Chen Proceedings of the 50th Annual IEEE/ACM International Symposium on …, 2017 | 37 | 2017 |
Whale: Efficient giant model training over heterogeneous {GPUs} X Jia, L Jiang, A Wang, W Xiao, Z Shi, J Zhang, X Li, L Chen, Y Li, ... 2022 USENIX Annual Technical Conference (USENIX ATC 22), 673-688, 2022 | 30 | 2022 |
Fusionstitching: boosting memory intensive computations for deep learning workloads Z Zheng, P Zhao, G Long, F Zhu, K Zhu, W Zhao, L Diao, J Yang, W Lin arXiv preprint arXiv:2009.10924, 2020 | 28 | 2020 |
DISC: A dynamic shape compiler for machine learning workloads K Zhu, WY Zhao, Z Zheng, TY Guo, PZ Zhao, JJ Bai, J Yang, XY Liu, ... Proceedings of the 1st Workshop on Machine Learning and Systems, 89-95, 2021 | 22 | 2021 |
Optimizing distributed training deployment in heterogeneous GPU clusters X Yi, S Zhang, Z Luo, G Long, L Diao, C Wu, Z Zheng, J Yang, W Lin Proceedings of the 16th International Conference on emerging Networking …, 2020 | 20 | 2020 |
Drew: Efficient winograd cnn inference with deep reuse R Wu, F Zhang, J Guan, Z Zheng, X Du, X Shen Proceedings of the ACM Web Conference 2022, 1807-1816, 2022 | 14 | 2022 |
Flash-llm: Enabling cost-effective and highly-efficient large generative model inference with unstructured sparsity H Xia, Z Zheng, Y Li, D Zhuang, Z Zhou, X Qiu, Y Li, W Lin, SL Song arXiv preprint arXiv:2309.10285, 2023 | 13 | 2023 |
Gopipe: a granularity-oblivious programming framework for pipelined stencil executions on gpu C Oh, Z Zheng, X Shen, J Zhai, Y Yi Proceedings of the ACM International Conference on Parallel Architectures …, 2020 | 13 | 2020 |
Exploring deep reuse in winograd CNN inference R Wu, F Zhang, Z Zheng, X Du, X Shen Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of …, 2021 | 11 | 2021 |
HiWayLib: A software framework for enabling high performance communications for heterogeneous pipeline computations Z Zheng, C Oh, J Zhai, X Shen, Y Yi, W Chen Proceedings of the Twenty-Fourth International Conference on Architectural …, 2019 | 10 | 2019 |
Auto-map: A DQN framework for exploring distributed execution plans for DNN workloads S Wang, Y Rong, S Fan, Z Zheng, LS Diao, G Long, J Yang, X Liu, W Lin arXiv preprint arXiv:2007.04069, 2020 | 8 | 2020 |
Optimizing DNN compilation for distributed training with joint OP and tensor fusion X Yi, S Zhang, L Diao, C Wu, Z Zheng, S Fan, S Wang, J Yang, W Lin IEEE Transactions on Parallel and Distributed Systems 33 (12), 4694-4706, 2022 | 4 | 2022 |
Whale: Scaling deep learning model training to the trillions X Jia, AW Le Jiang, J Zhang, X Li, W Xiao, Y Li, Z Zheng, X Liu, W Lin arXiv preprint arXiv:2011.09208, 2020 | 4 | 2020 |
Bladedisc: Optimizing dynamic shape machine learning workloads via compiler approach Z Zheng, Z Pan, D Wang, K Zhu, W Zhao, T Guo, X Qiu, M Sun, J Bai, ... Proceedings of the ACM on Management of Data 1 (3), 1-29, 2023 | 3 | 2023 |
Expanding the Edge: Enabling Efficient Winograd CNN Inference With Deep Reuse on Edge Device F Zhang, R Wu, J Guan, Z Zheng, X Guo, X Zhang, X Du, X Shen IEEE Transactions on Knowledge and Data Engineering, 2023 | 2 | 2023 |
Auto-parallelizing large models with rhino: A systematic approach on production ai platform S Zhang, L Diao, S Wang, Z Cao, Y Gu, C Si, Z Shi, Z Zheng, C Wu, W Lin arXiv preprint arXiv:2302.08141, 2023 | 2 | 2023 |