Tung D. Le
Tung D. Le
IBM Research - Tokyo
Verified email at jp.ibm.com - Homepage
Title
Cited by
Cited by
Year
Efficient query evaluation on distributed graphs with hadoop environment
LD Tung, Q Nguyen-Van, Z Hu
Proceedings of the Fourth Symposium on Information and Communication …, 2013
162013
Failure-aware Scheduling in Grid Computing Environments.
T Do, T Nguyen, DT Nguyen, HC Nguyen, T Le
GCA, 40-46, 2009
112009
Tflms: Large model support in tensorflow by graph rewriting
TD Le, H Imai, Y Negishi, K Kawachiya
arXiv preprint arXiv:1807.02037, 2018
102018
Towards systematic parallelization of graph transformations over Pregel
LD Tung, Z Hu
International Journal of Parallel Programming 45 (2), 320-339, 2017
102017
Minimizing data transfers for regular reachability queries on distributed graphs
Q Nguyen-Van, LD Tung, Z Hu
Proceedings of the Fourth Symposium on Information and Communication …, 2013
72013
Large model support for deep learning in caffe and chainer
M Cho, T Le, U Finkler, H Imai, Y Negishi, T Sekiyama, S Vinod, V Zolotov, ...
SysML, 2018
52018
Fast and accurate 3D medical image segmentation with data-swapping method
H Imai, S Matzek, TD Le, Y Negishi, K Kawachiya
arXiv preprint arXiv:1812.07816, 2018
42018
Pregel meets UnCAL: A systematic framework for transforming big graphs
LD Tung
2015 31st IEEE International Conference on Data Engineering Workshops, 250-254, 2015
42015
Automatic GPU memory management for large neural models in TensorFlow
TD Le, H Imai, Y Negishi, K Kawachiya
Proceedings of the 2019 ACM SIGPLAN International Symposium on Memory …, 2019
32019
An Intermediate Library for Multi-GPUs Computing Skeletons
T D. Le, NH Duc, PT Anh, NH Hoang, NM Thap
hgpu. org, 2012
32012
Involving CPUs into Multi-GPU Deep Learning
TD Le, T Sekiyama, Y Negishi, H Imai, K Kawachiya
Proceedings of the 2018 ACM/SPEC International Conference on Performance …, 2018
22018
Localizing tree-based convolutional neural networks
TD Le, T Sekiyama
US Patent App. 15/815,771, 2019
12019
Real-time resource usage reduction in artificial neural networks
T Sekiyama, K Kawachiya, TD Le, Y Negishi
US Patent 10,268,951, 2019
12019
Balancing memory consumption of multiple graphics processing units in deep learning
K Kawachiya, TD Le, Y Negishi
US Patent App. 15/808,370, 2018
12018
Derivation of parallel-efficient structural recursive functions from declarative graph queries
C Li, LD Tung, X Meng, Z Hu
Proceedings of the 31st Annual ACM Symposium on Applied Computing, 1922-1925, 2016
12016
Let high-level graph queries be parallel efficient: an approach over structural recursion on pregel
C Li, LD Tung, X Meng, Z Hu
Journal of Information Processing 24 (6), 928-936, 2016
12016
Real-time resource usage reduction in artificial neural networks
T Sekiyama, K Kawachiya, TD Le, Y Negishi
US Patent 10,558,914, 2020
2020
Mechanism for choosing execution mode for large neural network
Y Negishi, H Imai, T Sekiyama, TD Le, K Kawachiya
US Patent App. 16/018,680, 2019
2019
Large Data Flow Graphs in Limited GPU Memory
G Janssen, V Zolotov, TD Le
2019 IEEE International Conference on Big Data (Big Data), 1821-1830, 2019
2019
High Resolution Medical Image Segmentation Using Data-Swapping Method
H Imai, S Matzek, TD Le, Y Negishi, K Kawachiya
International Conference on Medical Image Computing and Computer-Assisted …, 2019
2019
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Articles 1–20