| TensorLayer: A versatile library for efficient deep learning development H Dong, A Supratak, L Mai, F Liu, A Oehmichen, S Yu, Y Guo ACM Multimedia (Best Open Source Software Award), 2017 | 128 | 2017 |
| NetAgg: Using middleboxes for application-specific on-path aggregation in data centres L Mai, L Rupprecht, A Alim, P Costa, M Migliavacca, P Pietzuch, AL Wolf International Conference on emerging Networking EXperiments and Technologies …, 2014 | 102 | 2014 |
| KungFu: Making training in distributed machine learning adaptive L Mai, G Li, M Wagenländer, K Fertakis, AO Brabete, P Pietzuch USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2020 | 88 | 2020 |
| CrossBow: Scaling deep learning with small batch sizes on multi-GPU servers A Koliousis, P Watcharapichat, M Weidlich, L Mai, P Costa, P Pietzuch International Conference on Very Large Data Bases (VLDB), 2019 | 86 | 2019 |
| Optimizing network performance in distributed machine learning L Mai, C Hong, P Costa USENIX Workshop on Hot Topics in Cloud Computing (HotCloud), 2015 | 84 | 2015 |
| Chi: A scalable and programmable control plane for distributed stream processing systems L Mai, K Zeng, R Potharaju, L Xu, S Suh, S Venkataraman, P Costa, T Kim, ... International Conference on Very Large Data Bases (VLDB), 2018 | 83 | 2018 |
| Emu: Rapid prototyping of networking services N Sultana, S Galea, D Greaves, M Wojcik, J Shipton, R Clegg, L Mai, ... USENIX Annual Technical Conference (ATC), 2017 | 44 | 2017 |
| ServerlessLLM: Low-latency serverless inference for large language models Y Fu, L Xue, Y Huang, AO Brabete, D Ustiugov, Y Patel, L Mai USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2024 | 37* | 2024 |
| Ekko: A large-scale deep learning recommender system with low-latency model update C Sima, Y Fu, MK Sit, L Guo, X Gong, F Lin, J Wu, Y Li, H Rong, PL Aublin, ... USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2022 | 37 | 2022 |
| Move fast and meet deadlines: Fine-grained real-time stream processing with Cameo L Xu, S Venkataraman, I Gupta, L Mai, R Potharaju USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2021 | 35 | 2021 |
| Large sequence models for sequential decision-making: a survey M Wen, R Lin, H Wang, Y Yang, Y Wen, L Mai, J Wang, H Zhang, ... Frontiers of Computer Science 17 (6), 176349, 2023 | 34 | 2023 |
| Spotnik: Designing distributed machine learning for transient cloud resources M Wagenländer, L Mai, G Li, P Pietzuch USENIX HotCloud, 2020 | 29 | 2020 |
| Taming hyper-parameters in deep learning systems L Mai, A Koliousis, G Li, AO Brabete, P Pietzuch SIGOPS Operating Systems Review (Invited Paper), 2019 | 28 | 2019 |
| Load balanced rendezvous data collection in wireless sensor networks L Mai, L Shangguan, C Lang, J Du, H Liu, Z Li IEEE MASS, 282-291, 2011 | 25 | 2011 |
| FLICK: Developing and running application-specific network services A Alim, RG Clegg, L Mai, L Rupprecht, E Seckler, P Costa, P Pietzuch, ... USENIX Annual Technical Conference (ATC), 2016 | 21 | 2016 |
| MoE-Infinity: Activation-aware expert offloading for efficient MoE serving L Xue, Y Fu, Z Lu, L Mai, M Marina arXiv preprint arXiv:2401.14361, 2024 | 20* | 2024 |
| Fast and flexible human pose estimation with HyperPose Y Guo, J Liu, G Li, L Mai, H Dong ACM Multimedia, 2021 | 19 | 2021 |
| Efficient reinforcement learning development with RLzoo Z Ding, T Yu, Y Huang, H Zhang, G Li, Q Guo, L Mai, H Dong ACM Multimedia, 2021 | 18* | 2021 |
| Rendezvous data collection using a mobile element in heterogeneous sensor networks J Du, H Liu, L Shangguan, L Mai, K Wang, S Li IJDSN, 2012 | 17 | 2012 |
| Exploiting time-malleability in cloud-based batch processing systems L Mai, E Kalyvianaki, P Costa ACM LADIS (Co-located with SOSP), 2013 | 16 | 2013 |
Peter PietzuchProfessor of Distributed Systems, Imperial College LondonVerified email at imperial.ac.uk
