Live virtual machine migration with adaptive, memory compression H Jin, L Deng, S Wu, X Shi, X Pan 2009 IEEE International Conference on Cluster Computing and Workshops, 1-10, 2009 | 466 | 2009 |
Evaluating mapreduce on virtual machines: The hadoop case S Ibrahim, H Jin, L Lu, L Qi, S Wu, X Shi Cloud Computing: First International Conference, CloudCom 2009, Beijing …, 2009 | 183 | 2009 |
Graph Processing on GPUs: A Survey X Shi, Z Zheng, Y Zhou, H Jin, L He, B Liu, QS Hua ACM Computing Surveys, 2018 | 175 | 2018 |
Capuchin: Tensor-based gpu memory management for deep learning X Peng, X Shi, H Dai, H Jin, W Ma, Q Xiong, F Yang, X Qian Proceedings of the Twenty-Fifth International Conference on Architectural …, 2020 | 173 | 2020 |
Optimizing the live migration of virtual machine by CPU scheduling H Jin, W Gao, S Wu, X Shi, X Wu, F Zhou Journal of Network and Computer Applications 34 (4), 1088-1096, 2011 | 121 | 2011 |
Towards optimized fine-grained pricing of IaaS cloud platform H Jin, X Wang, S Wu, S Di, X Shi IEEE Transactions on cloud Computing 3 (4), 436-448, 2014 | 105 | 2014 |
Tools and technologies for building clouds H Jin, S Ibrahim, T Bell, L Qi, H Cao, S Wu, X Shi Cloud computing: Principles, systems and applications, 3-20, 2010 | 90 | 2010 |
Semantic and syntactic enhanced aspect sentiment triplet extraction Z Chen, H Huang, B Liu, X Shi, H Jin Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 2021 | 69 | 2021 |
Lifetime-Based Memory Management for Distributed Data Processing Systems L Lu, X Shi, Y Zhou, X Zhang, H Jin, C Pei, L He, Y Geng PVLDB, 2016 | 67 | 2016 |
MECOM: Live migration of virtual machines by adaptively compressing memory pages H Jin, L Deng, S Wu, X Shi, H Chen, X Pan Future Generation Computer Systems 38, 23-35, 2014 | 66 | 2014 |
Safestack: Automatically patching stack-based buffer overflow vulnerabilities G Chen, H Jin, D Zou, BB Zhou, Z Liang, W Zheng, X Shi IEEE Transactions on Dependable and Secure Computing 10 (6), 368-379, 2013 | 66 | 2013 |
Mammoth: Gearing hadoop towards memory-intensive mapreduce applications X Shi, M Chen, L He, X Xie, L Lu, H Jin, Y Chen, S Wu IEEE Transactions on Parallel and Distributed Systems 26 (8), 2300-2315, 2015 | 61 | 2015 |
Core maintenance in dynamic graphs: A parallel approach based on matching H Jin, N Wang, D Yu, QS Hua, X Shi, X Xie IEEE Transactions on Parallel and Distributed Systems 29 (11), 2416-2428, 2018 | 55 | 2018 |
Frog: Asynchronous graph processing on GPU with hybrid coloring model X Shi, X Luo, J Liang, P Zhao, S Di, B He, H Jin IEEE Transactions on Knowledge and Data Engineering 30 (1), 29-42, 2017 | 54 | 2017 |
Layer-centric memory reuse and data migration for extreme-scale deep learning on many-core architectures H Jin, B Liu, W Jiang, Y Ma, X Shi, B He, S Zhao ACM Transactions on Architecture and Code Optimization (TACO) 15 (3), 1-26, 2018 | 52 | 2018 |
DAGMap: efficient and dependable scheduling of DAG workflow job in Grid H Cao, H Jin, X Wu, S Wu, X Shi The Journal of supercomputing 51, 201-223, 2010 | 52 | 2010 |
RTRM: A response time-based replica management strategy for cloud storage system X Bai, H Jin, X Liao, X Shi, Z Shao Grid and Pervasive Computing: 8th International Conference, GPC 2013 and …, 2013 | 48 | 2013 |
An adaptive meta-scheduler for data-intensive applications H Jin, X Shi, W Qiang, D Zou International Journal of Grid and Utility Computing 1 (1), 32-37, 2005 | 46 | 2005 |
Reveal training performance mystery between TensorFlow and PyTorch in the single GPU environment H Dai, X Peng, X Shi, L He, Q Xiong, H Jin Science China Information Sciences 65, 1-17, 2022 | 40 | 2022 |
The mapreduce programming model and implementations H Jin, S Ibrahim, L Qi, H Cao, S Wu, X Shi Cloud computing: principles and paradigms, 373-390, 2011 | 39 | 2011 |