Da Li
Da Li
Genies Inc.
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Cited by
Cited by
Evaluating the energy efficiency of deep convolutional neural networks on CPUs and GPUs
D Li, X Chen, M Becchi, Z Zong
2016 IEEE international conferences on big data and cloud computing (BDCloud …, 2016
Deploying Graph Algorithms on GPUs: an Adaptive Solution
D Li, M Becchi
2013 IEEE 27th International Symposium on Parallel & Distributed Processing …, 2013
Compiler-assisted Workload Consolidation for Efficient Dynamic Parallelism on GPU
H Wu, D Li, M Becchi
2016 IEEE 27th International Symposium on Parallel & Distributed Processing …, 2016
Nested Parallelism on GPU: Exploring Parallelization Templates for Irregular Loops and Recursive Computations
D Li, H Wu, M Becchi
International Conference on Parallel Processing, 2015
Learning convolution neural networks on heterogeneous cpu-gpu platform
M Liu, X Xu, D Li
US Patent 10,002,402, 2018
A Distributed CPU-GPU Framework for Pairwise Alignments on Large-Scale Sequence Datasets
D Li, K Sajjapongse, H Truong, G Conant, M Becchi
2013 IEEE 24th International Conference on Application-Specific Systems …, 2013
Multiple pairwise sequence alignments with the needleman-wunsch algorithm on gpu
D Li, M Becchi
2012 SC companion: high performance computing, networking storage and …, 2012
Large-Scale Pairwise Alignments on GPU Clusters: Exploring the Implementation Space
H Truong, D Li, K Sajjapongse, G Conant, M Becchi
Journal of Signal Processing Systems 77 (1-2), 131-149, 2014
GRapid: a Compilation and Runtime Framework for Rapid Prototyping of Graph Applications on Many-core Processors
D Li, S Chakradhar, M Becchi
2014 IEEE 20th International Conference on Parallel and Distributed Systems, 2014
Exploiting dynamic parallelism to efficiently support irregular nested loops on GPUs
D Li, H Wu, M Becchi
Proceedings of the 2015 International Workshop on Code Optimisation for …, 2015
Source-to-source transformations for graph processing on many-core platforms
S Chakradhar, M Becchi, D Li
US Patent 20,150,113,514, 2016
Fast integral histogram computations on GPU for real-time video analytics
M Poostchi, K Palaniappan, D Li, M Becchi, F Bunyak, G Seetharaman
arXiv preprint arXiv:1711.01919, 2017
GTF: An Adaptive Network Anomaly Detection Method at the Network Edge
R Li, Z Zhou, X Liu, D Li, W Yang, S Li, Q Liu
Security and Communication Networks 2021, 1-12, 2021
BPA:The Optimal Placement of Interdependent VNFs in Many-core System
Y Zhong, Z Zhou, X Liu, D Li, M Guo, S Zhang, Q Liu, L Guo
EAI CollaborateCom, 2020
Multi-source data oriented flexible real-time information fusion platform on FPGA
T Song, D Li, Y Yao
2011 International Conference on Electronics, Communications and Control …, 2011
A Scalable Cloud-based Architecture to Deploy JupyterHub for Computational Social Science Research
D Li, R Pyke, R Jiang
Practice and Experience in Advanced Research Computing, 2021
Facilitating emerging applications on many-core processors
D Li
University of Missouri--Columbia, 2016
Designing Code Variants for Applications with Nested Parallelism on GPUs
D Li, M Becchi
GPU Technology Conference, 2015
SASD: A Self-Adaptive Stateful Decompression Architecture
Z Zhou, X Zhang, Q Kiu, Y Zhu, D Li, L Guo
IEEE Global Communications Conference, 2018
T Song, D Li
CN Patent CN101,901,257 B, 2012
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