Feng Yan
Titel
Zitiert von
Zitiert von
Jahr
Terngrad: Ternary gradients to reduce communication in distributed deep learning
W Wen, C Xu, F Yan, C Wu, Y Wang, Y Chen, H Li
arXiv preprint arXiv:1705.07878, 2017
4842017
Practise: Robust prediction of data center time series
J Xue, F Yan, R Birke, LY Chen, T Scherer, E Smirni
2015 11th International Conference on Network and Service Management (CNSM …, 2015
632015
Performance modeling and scalability optimization of distributed deep learning systems
F Yan, O Ruwase, Y He, T Chilimbi
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge …, 2015
542015
Mark: Exploiting cloud services for cost-effective, slo-aware machine learning inference serving
C Zhang, M Yu, W Wang, F Yan
2019 {USENIX} Annual Technical Conference ({USENIX}{ATC} 19), 1049-1062, 2019
492019
Hyperdrive: Exploring hyperparameters with pop scheduling
J Rasley, Y He, F Yan, O Ruwase, R Fonseca
Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference, 1-13, 2017
282017
Multiple pools in a multi-core system
L Cherkasova, F Yan
US Patent App. 15/120,958, 2017
242017
Batchcrypt: Efficient homomorphic encryption for cross-silo federated learning
C Zhang, S Li, J Xia, W Wang, F Yan, Y Liu
2020 {USENIX} Annual Technical Conference ({USENIX}{ATC} 20), 493-506, 2020
202020
Grnn: Low-latency and scalable rnn inference on gpus
C Holmes, D Mawhirter, Y He, F Yan, B Wu
Proceedings of the Fourteenth EuroSys Conference 2019, 1-16, 2019
202019
SERF: efficient scheduling for fast deep neural network serving via judicious parallelism
F Yan, O Ruwase, Y He, E Smirni
SC'16: Proceedings of the International Conference for High Performance …, 2016
192016
Optimizing power and performance trade-offs of MapReduce job processing with heterogeneous multi-core processors
F Yan, L Cherkasova, Z Zhang, E Smirni
2014 IEEE 7th International Conference on Cloud Computing, 240-247, 2014
172014
Tifl: A tier-based federated learning system
Z Chai, A Ali, S Zawad, S Truex, A Anwar, N Baracaldo, Y Zhou, H Ludwig, ...
Proceedings of the 29th International Symposium on High-Performance Parallel …, 2020
162020
Efficient deep neural network serving: Fast and furious
F Yan, Y He, O Ruwase, E Smirni
IEEE Transactions on Network and Service Management 15 (1), 112-126, 2018
162018
Storage workload isolation via tier warming: How models can help
J Xue, F Yan, A Riska, E Smirni
11th International Conference on Autonomic Computing ({ICAC} 14), 1-11, 2014
162014
Fast eventual consistency with performance guarantees for distributed storage
F Yan, A Riska, E Smirni
2012 32nd International Conference on Distributed Computing Systems …, 2012
162012
Heterogeneous cores for mapreduce processing: Opportunity or challenge?
F Yan, L Cherkasova, Z Zhang, E Smirni
2014 IEEE Network Operations and Management Symposium (NOMS), 1-4, 2014
142014
Stay fresh: Speculative synchronization for fast distributed machine learning
C Zhang, H Tian, W Wang, F Yan
2018 IEEE 38th International Conference on Distributed Computing Systems …, 2018
122018
Toward accurate and efficient emulation of public blockchains in the cloud
X Wang, A Al-Mamun, F Yan, D Zhao
International Conference on Cloud Computing, 67-82, 2019
112019
Leasgd: an efficient and privacy-preserving decentralized algorithm for distributed learning
HP Cheng, P Yu, H Hu, F Yan, S Li, H Li, Y Chen
arXiv preprint arXiv:1811.11124, 2018
112018
Smoothout: Smoothing out sharp minima to improve generalization in deep learning
W Wen, Y Wang, F Yan, C Xu, C Wu, Y Chen, H Li
arXiv preprint arXiv:1805.07898, 2018
112018
Dyscale: a mapreduce job scheduler for heterogeneous multicore processors
F Yan, L Cherkasova, Z Zhang, E Smirni
IEEE Transactions on Cloud Computing, 2017
112017
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