Tung D. Le
Tung D. Le
IBM Research - Tokyo
Verified email at jp.ibm.com - Homepage
TitleCited byYear
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
152013
Towards systematic parallelization of graph transformations over Pregel
LD Tung, Z Hu
International Journal of Parallel Programming 45 (2), 320-339, 2017
92017
Failure-aware Scheduling in Grid Computing Environments.
T Do, T Nguyen, DT Nguyen, HC Nguyen, T Le
GCA, 40-46, 2009
92009
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
52013
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
32018
Pregel meets UnCAL: A systematic framework for transforming big graphs
LD Tung
2015 31st IEEE International Conference on Data Engineering Workshops, 250-254, 2015
32015
An Intermediate Library for Multi-GPUs Computing Skeletons
T D. Le, NH Duc, PT Anh, NH Hoang, NM Thap
hgpu. org, 2012
32012
Tflms: Large model support in tensorflow by graph rewriting
TD Le, H Imai, Y Negishi, K Kawachiya
arXiv preprint arXiv:1807.02037, 2018
22018
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
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 App. 16/293,700, 2019
2019
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
2019
Multi-gpu deep learning using cpus
TD Le, H Imai, T Sekiyama, Y Negishi
US Patent App. 15/843,244, 2019
2019
Localizing tree-based convolutional neural networks
TD Le, T Sekiyama
US Patent App. 15/815,771, 2019
2019
Real-time resource usage reduction in artificial neural networks
T Sekiyama, K Kawachiya, TD Le, Y Negishi
US Patent App. 10/268,951, 2019
2019
Real-time resource usage reduction in artificial neural networks
T Sekiyama, K Kawachiya, TD Le, Y Negishi
US Patent App. 15/622,127, 2018
2018
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
2018
Optimizing tree-based convolutional neural networks
TD Le, T Sekiyama, K Zhao
US Patent App. 15/903,600, 2018
2018
Optimizing tree-based convolutional neural networks
TD Le, T Sekiyama, K Zhao
US Patent App. 15/617,737, 2018
2018
The system can't perform the operation now. Try again later.
Articles 1–20