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
172013
Tflms: Large model support in tensorflow by graph rewriting
TD Le, H Imai, Y Negishi, K Kawachiya
arXiv preprint arXiv:1807.02037, 2018
132018
Towards systematic parallelization of graph transformations over pregel
LD Tung, Z Hu
International Journal of Parallel Programming 45 (2), 320-339, 2017
112017
Failure-aware Scheduling in Grid Computing Environments.
T Do, T Nguyen, DT Nguyen, HC Nguyen, T Le
GCA, 40-46, 2009
112009
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
82018
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
82013
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
52019
Large model support for deep learning in caffe and chainer
M Cho, TD Le, U Finkler, H Imai, Y Negishi, T Sekiyama, S Vinod, ...
SysML, 2018
52018
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
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
32018
An Intermediate Library for Multi-GPUs Computing Skeletons
T D. Le, NH Duc, PT Anh, NH Hoang, NM Thap
hgpu. org, 2012
32012
Profiling based out-of-core hybrid method for large neural networks: poster
Y Ito, H Imai, TL Duc, Y Negishi, K Kawachiya, R Matsumiya, T Endo
Proceedings of the 24th Symposium on Principles and Practice of Parallel …, 2019
22019
Profiling based out-of-core hybrid method for large neural networks
Y Ito, H Imai, TL Duc, Y Negishi, K Kawachiya, R Matsumiya, T Endo
arXiv preprint arXiv:1907.05013, 2019
12019
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
Compiling ONNX Neural Network Models Using MLIR
TD Le, GT Bercea, T Chen, AE Eichenberger, H Imai, T Jin, K Kawachiya, ...
arXiv preprint arXiv:2008.08272, 2020
2020
Real-time resource usage reduction in artificial neural networks
T Sekiyama, K Kawachiya, TD Le, Y Negishi
US Patent 10,558,914, 2020
2020
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Articles 1–20