Stebėti
Jinliang Wei
Jinliang Wei
Google; Carnegie Mellon University
Patvirtintas el. paštas alumni.cmu.edu - Pagrindinis puslapis
Pavadinimas
Cituota
Cituota
Metai
Petuum: A new platform for distributed machine learning on big data
EP Xing, Q Ho, W Dai, JK Kim, J Wei, S Lee, X Zheng, P Xie, A Kumar, ...
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge …, 2015
6542015
Poseidon: An efficient communication architecture for distributed deep learning on {GPU} clusters
H Zhang, Z Zheng, S Xu, W Dai, Q Ho, X Liang, Z Hu, J Wei, P Xie, ...
2017 {USENIX} Annual Technical Conference ({USENIX}{ATC} 17), 181-193, 2017
501*2017
Lightlda: Big topic models on modest computer clusters
J Yuan, F Gao, Q Ho, W Dai, J Wei, X Zheng, EP Xing, TY Liu, WY Ma
Proceedings of the 24th International Conference on World Wide Web, 1351-1361, 2015
2402015
Exploiting bounded staleness to speed up big data analytics
H Cui, J Cipar, Q Ho, JK Kim, S Lee, A Kumar, J Wei, W Dai, GR Ganger, ...
2014 USENIX Annual Technical Conference (USENIX ATC 14), 37-48, 2014
2122014
Priority-based parameter propagation for distributed DNN training
A Jayarajan, J Wei, G Gibson, A Fedorova, G Pekhimenko
Proceedings of the 2 nd SysML Conference, Palo Alto, CA, USA, 2019., 2019
1922019
High-performance distributed ML at scale through parameter server consistency models
W Dai, A Kumar, J Wei, Q Ho, G Gibson, E Xing
Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015
1692015
Addressing the straggler problem for iterative convergent parallel ML
A Harlap, H Cui, W Dai, J Wei, GR Ganger, PB Gibbons, GA Gibson, ...
Proceedings of the Seventh ACM Symposium on Cloud Computing, 98-111, 2016
1652016
Dorylus: Affordable, Scalable, and Accurate {GNN} Training with Distributed {CPU} Servers and Serverless Threads
J Thorpe, Y Qiao, J Eyolfson, S Teng, G Hu, Z Jia, J Wei, K Vora, ...
15th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2021
1522021
Managed communication and consistency for fast data-parallel iterative analytics
J Wei, W Dai, A Qiao, Q Ho, H Cui, GR Ganger, PB Gibbons, GA Gibson, ...
Proceedings of the Sixth ACM Symposium on Cloud Computing, 381-394, 2015
1492015
Exploiting iterative-ness for parallel ML computations
H Cui, A Tumanov, J Wei, L Xu, W Dai, J Haber-Kucharsky, Q Ho, ...
Proceedings of the ACM Symposium on Cloud Computing, 1-14, 2014
492014
Overlap Communication with Dependent Computation via Decomposition in Large Deep Learning Models
S Wang, J Wei, A Sabne, A Davis, B Ilbeyi, B Hechtman, D Chen, ...
Proceedings of the 28th ACM International Conference on Architectural …, 2022
482022
Petuum: A framework for iterative-convergent distributed ml
W Dai, J Wei, X Zheng, JK Kim, S Lee, J Yin, Q Ho, EP Xing
arXiv preprint arXiv:1312.7651 1 (2.1), 2013
432013
Automating Dependence-Aware Parallelization of Machine Learning Training on Distributed Shared Memory
J Wei, G Gibson, P Gibbons, XP Eric
EuroSys, 2019
122019
Consistent bounded-asynchronous parameter servers for distributed ML
J Wei, W Dai, A Kumar, X Zheng, Q Ho, EP Xing
arXiv preprint arXiv:1312.7869, 2013
122013
Benchmarking apache spark with machine learning applications
J Wei, JK Kim, GA Gibson
Parallel Data Lab., Carnegie Mellon Univ., Pittsburgh, PA, USA, 2016
92016
Parallel implementation of expectation-maximization for fast convergence
H Cui, J Wei, W Dai
ACM proceedings, 2010
62010
A software toolkit for visualizing enterprise routing design
X Sun, J Wei, SG Rao, GG Xie
2011 4th Symposium on Configuration Analytics and Automation (SAFECONFIG), 1-8, 2011
32011
Dynamic scheduling for dynamic control flow in deep learning systems
J Wei, G Gibson, V Vasudevan, E Xing
URL http://www. cs. cmu. edu/jinlianw/papers/dynamic_scheduling_nips18_sysml …, 0
3*
Scheduling for Efficient Large-Scale Machine Learning Training
J Wei
Intel, 2019
12019
Addressing the Long-Lineage Bottleneck in Apache Spark
H Wang, J Wei, G Gibson
12018
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Straipsniai 1–20