Publications

Yao Fu, Leyang Xue, Yeqi Huang, Andrei-Octavian Brabete, Dmitrii Ustiugov, Yuvraj Patel, Luo Mai (2024). ServerlessLLM: Locality-Enhanced Serverless Inference for Large Language Models. In OSDI.

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Leyang Xue, Yao Fu, Zhan Lu, Luo Mai, Mahesh Marina (2024). MoE-Infinity: Activation-Aware Expert Offloading for Efficient MoE Serving. In Arxiv.

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Jie Ren*, Xidong Feng*, Bo Liu*, Xuehai Pan*, Yao Fu, Luo Mai, Yaodong Yang (2023). TorchOpt: An Efficient Library for Differentiable Optimization. In JMLR.

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Hanjing Wang*, Man-Kit Sit*, Congjie He, Ying Wen, Weinan Zhang, Jun Wang, Yaodong Yang, Luo Mai (2023). GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models. In ICML.

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Zeyuan Tan*, Xiulong Yuan*, Congjie He*, Man-Kit Sit, Guo Li, Xiaoze Liu, Baole Ai, Kai Zeng, Peter Pietzuch, Luo Mai (2023). Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness. In Arxiv.

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Chijun Sima*, Yao Fu*, Man-Kit Sit, Liyi Guo, Xuri Gong, Feng Lin, Junyu Wu, Yongsheng Li, Haidong Rong, Pierre-Louis Aublin, Luo Mai (2022). Ekko: A Large-Scale Deep Learning Recommender System with Low-Latency Model Update. In USENIX OSDI.

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Bo Liu, Xidong Feng, Jie Ren, Luo Mai, Rui Zhu, Haifeng Zhang, Jun Wang, Yaodong Yang (2022). A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning. In NeurIPS.

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Jie Ren*, Wenteng Liang*, Ran Yan, Luo Mai, Shiwen Liu, Xiao Liu (2022). MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment. In ECCV.

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Le Xu, Shivaram Venkataraman, Indranil Gupta, Luo Mai, Rahul Potharaju (2021). Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo. In USENIX NSDI.

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Yixiao Guo, Jiawei Liu, Guo Li, Luo Mai, Hao Dong (2021). Fast and Flexible Human Pose Estimation with HyperPose. In ACM Multimedia (Open-source Software Competition).

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Zihan Ding, Tianyang Yu, Yanhua Huang, Hongming Zhang, Guo Li, Quancheng Guo, Luo Mai, Hao Dong (2021). Efficient Reinforcement Learning Development with RLzoo. In ACM Multimedia (Open-source Software Competition).

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Luo Mai, Guo Li, Marcel Wagenlander, Konstantinos Fertakis, Andrei-Octavian Brabete, Peter Pietzuch (2020). KungFu: Making Training in Distributed Machine Learning Adaptive. In USENIX OSDI.

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Marcel Wagenlander, Luo Mai, Guo Li, Peter Pietzuch (2020). Spotnik: Designing Distributed Machine Learning for Transient Cloud Resources. In USENIX HotCloud.

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Alexandros Koliousis, Pijika Watcharapichat, Matthias Weidlich, Luo Mai, Paolo Costa, Peter Pietzuch (2019). CrossBow: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers. In VLDB.

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Luo Mai, Alexandros Koliousis, Guo Li, Andrei-Octavian Brabete, Peter Pietzuch (2019). Taming Hyper-parameters in Deep Learning Systems. In ACM SIGOPS Operating Systems Review (Invited Paper).

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Luo Mai, Kai Zeng, Rahul Potharaju, Le Xu, Shivaram Venkataraman, Paolo Costa, Terry Kim, Saravanan Muthukrishnan, Vamsi Kuppa, Sudheer Dhulipalla, Sriram Rao (2018). Chi: A Scalable and Programmable Control Plane for Distributed Stream Processing Systems. In VLDB.

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Nik Sultana, Salvator Galea, David Greaves, Marcin Wojcik, Jonny Shipton, Richard Clegg, Luo Mai, Pietro Bressana, Robert Soule, Richard Mortier, Paolo Costa, Peter Pietzuch, Jon Crowcroft, Andrew W Moore, Noa Zilberman (2017). Emu: Rapid Prototyping of Networking Services. In USENIX ATC.

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Hao Dong, Akara Supratak, Luo Mai, Fangde Liu, Axel Oehmichen, Simiao Yu, Yike Guo (2017). TensorLayer: A Versatile Library for Efficient Deep Learning Development. In ACM Multimedia (Best Open-source Software Award).

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Abdul Alim, Richard G. Clegg, Luo Mai, Lukas Rupprecht, Eric Seckler, Paolo Costa, Peter Pietzuch, Alexander L. Wolf, Nik Sultana, Jon Crowcroft, Anil Madhavapeddy, Andrew Moore, Richard Mortier, Luis Oviedo, Masoud Koleni, Derek McAuley, Matteo Migliavacca (2016). Flick: Developing and Running Application-specific Network Services . In USENIX ATC.

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Da Yu, Luo Mai, Somaya Arianfar, Rodrigo Fonseca, Orran Krieger, David Oran (2016). Towards a Network Marketplace in a Cloud. In USENIX HotCloud.

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Luo Mai, Chuntao Hong, Paolo Costa (2015). Optimizing Network Performance in Distributed Machine Learning. In USENIX HotCloud.

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Luo Mai, Lukas Rupprecht, Abdul Alim, Paolo Costa, Matteo Migliavacca, Peter Pietzuch, Alexander L. Wolf (2014). NetAgg: Using Middleboxes for Application-specific On-path Aggregation in Data Centres. In ACM CoNEXT (Best Paper Finalist).

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Luo Mai, Evangelia Kalyvianaki, Paolo Costa (2013). Exploiting Time-malleability in Cloud-based Batch Processing Systems. In ACM LADIS.

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Luo Mai, Longfei Shangguan, Chao Lang, Junzhao Du, Zhenjiang Li, Mo Li (2011). Load Balanced Rendezvous Data Collection in Wireless Sensor Networks. In IEEE MASS (Best Paper Finalist).

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