Author of the publication

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling.

C. Wan, Y. Li, A. Li, N. Kim, and Y. Lin. MLSys, mlsys.org, (2022)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Li Li

Other publications of authors with the same name

Visage: enabling timely analytics for drone imagery.S. Jha, Y. Li, S. Noghabi, V. Ranganathan, P. Kumar, A. Nelson, M. Toelle, S. Sinha, R. Chandra, and A. Badam. MobiCom, page 789-803. ACM, (2021)GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training.M. Yu, Z. Lin, K. Narra, S. Li, Y. Li, N. Kim, A. Schwing, M. Annavaram, and S. Avestimehr. NeurIPS, page 5129-5139. (2018)PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication.C. Wan, Y. Li, C. Wolfe, A. Kyrillidis, N. Kim, and Y. Lin. ICLR, OpenReview.net, (2022)BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling.C. Wan, Y. Li, A. Li, N. Kim, and Y. Lin. MLSys, mlsys.org, (2022)Liquid state machine based pattern recognition on FPGA with firing-activity dependent power gating and approximate computing.Q. Wang, Y. Li, and P. Li. ISCAS, page 361-364. IEEE, (2016)DeepStore: In-Storage Acceleration for Intelligent Queries.V. Mailthody, Z. Qureshi, W. Liang, Z. Feng, S. Gonzalo, Y. Li, H. Franke, J. Xiong, J. Huang, and W. Hwu. MICRO, page 224-238. ACM, (2019)Accelerating distributed reinforcement learning with in-switch computing.Y. Li, I. Liu, Y. Yuan, D. Chen, A. Schwing, and J. Huang. ISCA, page 279-291. ACM, (2019)Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training.Y. Li, M. Yu, S. Li, S. Avestimehr, N. Kim, and A. Schwing. NeurIPS, page 8056-8067. (2018)Doing more with less: training large DNN models on commodity servers for the masses.Y. Li, A. Phanishayee, D. Murray, and N. Kim. HotOS, page 119-127. ACM, (2021)A Network-Centric Hardware/Algorithm Co-Design to Accelerate Distributed Training of Deep Neural Networks.Y. Li, J. Park, M. Alian, Y. Yuan, Z. Qu, P. Pan, R. Wang, A. Schwing, H. Esmaeilzadeh, and N. Kim. MICRO, page 175-188. IEEE Computer Society, (2018)

BibSonomy

Disambiguation of "Li, Youjie"

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling.

Please choose a person to relate this publication to

Li Li

Li Li

Li Li

Li Li

Li Li

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Li, Youjie"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling.

Please choose a person to relate this publication to

Li Li

Li Li

Li Li

Li Li

Li Li

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling.