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ORCIDHang Liu 0001
刘航
Person information
- unicode name: 刘航
- affiliation: Stevens Institute of Technology, HPDA lab, Hoboken, NJ, USA
- affiliation (former): University of Massachusetts Lowell, MA, USA
- affiliation (former, PhD 2017): George Washington University, Washington, DC, USA
Other persons with the same name
- Hang Liu — disambiguation page
- Hang Liu 0002
![[0000-0002-9736-3486]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0002-9736-3486")
— Chongqing University, College of Computer Science, China - Hang Liu 0003 — Catholic University of America, Electrical Engineering and Computer Science Department, Washington, DC, USA (and 1 more)
- Hang Liu 0004 — Shanghai University, School of Mechatronic Engineering and Automation, China
- Hang Liu 0005 — Beijing Jiaotong University, School of Computer and Information Technology, China
- Hang Liu 0006 — Singapore University of Technology and Design, Engineering Product Development Pillar (and 1 more)
- Hang Liu 0007 — Chinese University of Hong Kong, Department of Information Engineering, Shatin, Hong Kong
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2020 – today
- 2024
[j14]Santosh Pandey, Amir Yazdanbakhsh, Hang Liu:
TAO: Re-Thinking DL-based Microarchitecture Simulation. Proc. ACM Meas. Anal. Comput. Syst. 8(2): 28:1-28:25 (2024)- [j13]Chengying Huan, Yongchao Liu, Heng Zhang, Shuaiwen Song, Santosh Pandey, Shiyang Chen, Xiangfei Fang, Yue Jin, Baptiste Lepers, Yanjun Wu, Hang Liu:
TEA+: A Novel Temporal Graph Random Walk Engine with Hybrid Storage Architecture. ACM Trans. Archit. Code Optim. 21(2): 37 (2024) - [j12]Chengying Huan, Yongchao Liu, Heng Zhang, Hang Liu, Shiyang Chen, Shuaiwen Leon Song, Yanjun Wu:
TeGraph+: Scalable Temporal Graph Processing Enabling Flexible Edge Modifications. IEEE Trans. Parallel Distributed Syst. 35(8): 1469-1487 (2024) - [c52]Santosh Pandey, Amir Yazdanbakhsh, Hang Liu:
TAO: Re-Thinking DL-based Microarchitecture Simulation. SIGMETRICS/Performance (Abstracts) 2024: 23-24 - [i26]Bingbing Li, Geng Yuan, Zigeng Wang, Shaoyi Huang, Hongwu Peng, Payman Behnam, Wujie Wen, Hang Liu, Caiwen Ding:
Zero-Space Cost Fault Tolerance for Transformer-based Language Models on ReRAM. CoRR abs/2401.11664 (2024) - [i25]Santosh Pandey, Amir Yazdanbakhsh, Hang Liu:
Tao: Re-Thinking DL-based Microarchitecture Simulation. CoRR abs/2404.10921 (2024) - 2023
- [j11]Shilong Wang, Hang Liu, Anil Gaihre, Hengyong Yu:
ezLDA: Efficient and Scalable LDA on GPUs. IEEE Access 11: 100165-100179 (2023) - [j10]Yifei Wang, Shiyang Chen, Guobin Chen, Ethan Shurberg, Hang Liu, Pengyu Hong:
Motif-Based Graph Representation Learning with Application to Chemical Molecules. Informatics 10(1): 8 (2023) - [c51]Chengying Huan, Shuaiwen Leon Song, Santosh Pandey, Hang Liu, Yongchao Liu, Baptiste Lepers, Changhua He, Kang Chen, Jinlei Jiang, Yongwei Wu:
TEA: A General-Purpose Temporal Graph Random Walk Engine. EuroSys 2023: 182-198 - [c50]Andy Trinh, Shivam Sheth, Anil Gaihre, Caiwen Ding, Jieyang Chen, Feiyi Wang, David Pugmire, Scott Klasky, Hang Liu, Lipeng Wan:
Understanding Node Allocation on Leadership-Class Supercomputers with Graph Analytics. HPCC/DSS/SmartCity/DependSys 2023: 780-787 - [c49]Wang Feng, Shiyang Chen, Hang Liu, Yuede Ji:
PeeK: A Prune-Centric Approach for K Shortest Path Computation. SC 2023: 18:1-18:14 - [c48]Shiyang Chen, Da Zheng, Caiwen Ding, Chengying Huan, Yuede Ji, Hang Liu:
TANGO: re-thinking quantization for graph neural network training on GPUs. SC 2023: 38:1-38:14 - [i24]Shiyang Chen, Da Zheng, Caiwen Ding, Chengying Huan, Yuede Ji, Hang Liu:
Tango: rethinking quantization for graph neural network training on GPUs. CoRR abs/2308.00890 (2023) - [i23]Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, Pete Luferenko, Divya Kumar, Jonathan A. Weyn, Ruixiong Zhang, Sylwester Klocek, Volodymyr Vragov, Mohammed AlQuraishi, Gustaf Ahdritz, Christina Floristean, Cristina Negri, Rao Kotamarthi, Venkatram Vishwanath, Arvind Ramanathan, Sam Foreman, Kyle Hippe, Troy Arcomano, Romit Maulik, Maxim Zvyagin, Alexander Brace, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael W. Irvin, J. Gregory Pauloski, Logan T. Ward, Valérie Hayot-Sasson, Murali Emani, Zhen Xie, Diangen Lin, Maulik Shukla, Ian T. Foster, James J. Davis, Michael E. Papka, Thomas S. Brettin, Prasanna Balaprakash, Gina Tourassi, John Gounley, Heidi A. Hanson, Thomas E. Potok, Massimiliano Lupo Pasini, Kate Evans, Dan Lu, Dalton D. Lunga, Junqi Yin, Sajal Dash, Feiyi Wang, Mallikarjun Shankar, Isaac Lyngaas, Xiao Wang, Guojing Cong, Pei Zhang, Ming Fan, Siyan Liu, Adolfy Hoisie, Shinjae Yoo, Yihui Ren, William Tang, Kyle Felker, Alexey Svyatkovskiy, Hang Liu, Ashwin M. Aji, Angela Dalton, Michael J. Schulte, Karl Schulz, Yuntian Deng, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Anima Anandkumar, Rick Stevens:
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies. CoRR abs/2310.04610 (2023) - 2022
- [j9]Lingda Li, Santosh Pandey, Thomas Flynn, Hang Liu, Noel Wheeler, Adolfy Hoisie:
SimNet: Accurate and High-Performance Computer Architecture Simulation using Deep Learning. Proc. ACM Meas. Anal. Comput. Syst. 6(2): 25:1-25:24 (2022) - [j8]Yuede Ji, Hang Liu, Yang Hu, H. Howie Huang:
iSpan: Parallel Identification of Strongly Connected Components with Spanning Trees. ACM Trans. Parallel Comput. 9(3): 13:1-13:27 (2022) - [j7]Anil Gaihre, Xiaoye Sherry Li, Hang Liu:
gSoFa: Scalable Sparse Symbolic LU Factorization on GPUs. IEEE Trans. Parallel Distributed Syst. 33(4): 1015-1026 (2022) - [j6]Bolong Zheng, Xi Zhao, Lianggui Weng, Quoc Viet Hung Nguyen, Hang Liu, Christian S. Jensen:
PM-LSH: a fast and accurate in-memory framework for high-dimensional approximate NN and closest pair search. VLDB J. 31(6): 1339-1363 (2022) - [c47]Chengying Huan, Shuaiwen Leon Song, Yongchao Liu, Heng Zhang, Hang Liu, Charles He, Kang Chen, Jinlei Jiang, Yongwei Wu:
T-GCN: A Sampling Based Streaming Graph Neural Network System with Hybrid Architecture. PACT 2022: 69-82 - [c46]Shaoyi Huang, Dongkuan Xu, Ian En-Hsu Yen, Yijue Wang, Sung-En Chang, Bingbing Li, Shiyang Chen, Mimi Xie, Sanguthevar Rajasekaran, Hang Liu, Caiwen Ding:
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm. ACL (1) 2022: 190-200 - [c45]Hongwu Peng, Shaoyi Huang, Shiyang Chen, Bingbing Li, Tong Geng, Ang Li, Weiwen Jiang, Wujie Wen, Jinbo Bi, Hang Liu, Caiwen Ding:
A length adaptive algorithm-hardware co-design of transformer on FPGA through sparse attention and dynamic pipelining. DAC 2022: 1135-1140 - [c44]Chengying Huan, Hang Liu, Mengxing Liu, Yongchao Liu, Changhua He, Kang Chen, Jinlei Jiang, Yongwei Wu, Shuaiwen Leon Song:
TeGraph: A Novel General-Purpose Temporal Graph Computing Engine. ICDE 2022: 578-592 - [c43]Heng Zhang, Lingda Li, Hang Liu, Donglin Zhuang, Rui Liu, Chengying Huan, Shuang Song, Dingwen Tao, Yongchao Liu, Charles He, Yanjun Wu, Shuaiwen Leon Song:
Bring orders into uncertainty: enabling efficient uncertain graph processing via novel path sampling on multi-accelerator systems. ICS 2022: 11:1-11:14 - [c42]Yijue Wang, Jieren Deng, Dan Guo, Chenghong Wang, Xianrui Meng, Hang Liu, Chao Shang, Binghui Wang, Qin Cao, Caiwen Ding, Sanguthevar Rajasekaran:
Variance of the Gradient Also Matters: Privacy Leakage from Gradients. IJCNN 2022: 1-8 - [c41]Santosh Pandey, Lingda Li, Thomas Flynn, Adolfy Hoisie, Hang Liu:
Scalable Deep Learning-Based Microarchitecture Simulation on GPUs. SC 2022: 79:1-79:15 - [c40]Lingda Li, Santosh Pandey, Thomas Flynn, Hang Liu, Noel Wheeler, Adolfy Hoisie:
SimNet: Accurate and High-Performance Computer Architecture Simulation using Deep Learning. SIGMETRICS (Abstracts) 2022: 67-68 - [i22]Hongwu Peng, Shaoyi Huang, Shiyang Chen, Bingbing Li, Tong Geng, Ang Li, Weiwen Jiang, Wujie Wen, Jinbo Bi, Hang Liu, Caiwen Ding:
A Length Adaptive Algorithm-Hardware Co-design of Transformer on FPGA Through Sparse Attention and Dynamic Pipelining. CoRR abs/2208.03646 (2022) - [i21]Yifei Wang, Shiyang Chen, Guobin Chen, Ethan Shurberg, Hang Liu, Pengyu Hong:
Motif-based Graph Representation Learning with Application to Chemical Molecules. CoRR abs/2208.04529 (2022) - 2021
- [j5]Asma Aloufi, Peizhao Hu, Hang Liu, Sherman S. M. Chow, Kim-Kwang Raymond Choo:
Universal location referencing and homomorphic evaluation of geospatial query. Comput. Secur. 102: 102137 (2021) - [j4]Yu Xiang, Hang Liu, Tian Lan, H. Howie Huang, Suresh Subramaniam:
Optimizing Job Reliability Through Contention-Free, Distributed Checkpoint Scheduling. IEEE Trans. Netw. Serv. Manag. 18(2): 2077-2088 (2021) - [j3]Santosh Pandey, Zhibin Wang, Sheng Zhong, Chen Tian, Bolong Zheng, Xiaoye S. Li, Lingda Li, Adolfy Hoisie, Caiwen Ding, Dong Li, Hang Liu:
Trust: Triangle Counting Reloaded on GPUs. IEEE Trans. Parallel Distributed Syst. 32(11): 2646-2660 (2021) - [c39]Hongwu Peng, Shanglin Zhou, Scott Weitze, Jiaxin Li, Sahidul Islam, Tong Geng, Ang Li, Wei Zhang, Minghu Song, Mimi Xie, Hang Liu, Caiwen Ding:
Binary Complex Neural Network Acceleration on FPGA : (Invited Paper). ASAP 2021: 85-92 - [c38]Jieren Deng, Yijue Wang, Ji Li, Chenghong Wang, Chao Shang, Hang Liu, Sanguthevar Rajasekaran, Caiwen Ding:
TAG: Gradient Attack on Transformer-based Language Models. EMNLP (Findings) 2021: 3600-3610 - [c37]Zhen Xie, Wenqian Dong, Jiawen Liu, Hang Liu, Dong Li:
Tahoe: tree structure-aware high performance inference engine for decision tree ensemble on GPU. EuroSys 2021: 426-440 - [c36]Shaoyi Huang, Shiyang Chen, Hongwu Peng, Daniel Manu, Zhenglun Kong, Geng Yuan, Lei Yang, Shusen Wang, Hang Liu, Caiwen Ding:
HMC-TRAN: A Tensor-core Inspired Hierarchical Model Compression for Transformer-based DNNs on GPU. ACM Great Lakes Symposium on VLSI 2021: 169-174 - [c35]Hongwu Peng, Shiyang Chen, Zhepeng Wang, Junhuan Yang, Scott A. Weitze, Tong Geng, Ang Li, Jinbo Bi, Minghu Song, Weiwen Jiang, Hang Liu, Caiwen Ding:
Optimizing FPGA-based Accelerator Design for Large-Scale Molecular Similarity Search (Special Session Paper). ICCAD 2021: 1-7 - [c34]Yijue Wang, Chenghong Wang, Zigeng Wang, Shanglin Zhou, Hang Liu, Jinbo Bi, Caiwen Ding, Sanguthevar Rajasekaran:
Against Membership Inference Attack: Pruning is All You Need. IJCAI 2021: 3141-3147 - [c33]Geng Yuan, Payman Behnam, Zhengang Li, Ali Shafiee, Sheng Lin, Xiaolong Ma, Hang Liu, Xuehai Qian, Mahdi Nazm Bojnordi, Yanzhi Wang, Caiwen Ding:
FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Accelerator. ISCA 2021: 265-278 - [c32]Hongwu Peng, Shaoyi Huang, Tong Geng, Ang Li, Weiwen Jiang, Hang Liu, Shusen Wang, Caiwen Ding:
Accelerating Transformer-based Deep Learning Models on FPGAs using Column Balanced Block Pruning. ISQED 2021: 142-148 - [c31]Shiyang Chen, Shaoyi Huang, Santosh Pandey, Bingbing Li, Guang R. Gao, Long Zheng, Caiwen Ding, Hang Liu:
E.T.: re-thinking self-attention for transformer models on GPUs. SC 2021: 25 - [c30]Anil Gaihre, Da Zheng, Scott Weitze, Lingda Li, Shuaiwen Leon Song, Caiwen Ding, Xiaoye S. Li, Hang Liu:
Dr. Top-k: delegate-centric Top-k on GPUs. SC 2021: 39 - [i20]Jieren Deng, Yijue Wang, Ji Li, Chao Shang, Hang Liu, Sanguthevar Rajasekaran, Caiwen Ding:
TAG: Transformer Attack from Gradient. CoRR abs/2103.06819 (2021) - [i19]Santosh Pandey, Zhibin Wang, Sheng Zhong, Chen Tian, Bolong Zheng, Xiaoye S. Li, Lingda Li, Adolfy Hoisie, Caiwen Ding, Dong Li, Hang Liu:
TRUST: Triangle Counting Reloaded on GPUs. CoRR abs/2103.08053 (2021) - [i18]Lingda Li, Santosh Pandey, Thomas Flynn, Hang Liu, Noel Wheeler, Adolfy Hoisie:
SimNet: Computer Architecture Simulation using Machine Learning. CoRR abs/2105.05821 (2021) - [i17]Geng Yuan, Payman Behnam, Zhengang Li, Ali Shafiee, Sheng Lin, Xiaolong Ma, Hang Liu, Xuehai Qian, Mahdi Nazm Bojnordi, Yanzhi Wang, Caiwen Ding:
FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Accelerator. CoRR abs/2106.09144 (2021) - [i16]Bolong Zheng, Xi Zhao, Lianggui Weng, Nguyen Quoc Viet Hung, Hang Liu, Christian S. Jensen:
PM-LSH: a fast and accurate in-memory framework for high-dimensional approximate NN and closest pair search. CoRR abs/2107.05537 (2021) - [i15]Hongwu Peng, Shanglin Zhou, Scott Weitze, Jiaxin Li, Sahidul Islam, Tong Geng, Ang Li, Wei Zhang, Minghu Song, Mimi Xie, Hang Liu, Caiwen Ding:
Binary Complex Neural Network Acceleration on FPGA. CoRR abs/2108.04811 (2021) - [i14]Hongwu Peng, Shiyang Chen, Zhepeng Wang, Junhuan Yang, Scott A. Weitze, Tong Geng, Ang Li, Jinbo Bi, Minghu Song, Weiwen Jiang, Hang Liu, Caiwen Ding:
Optimizing FPGA-based Accelerator Design for Large-Scale Molecular Similarity Search. CoRR abs/2109.06355 (2021) - [i13]Anil Gaihre, Da Zheng, Scott Weitze, Lingda Li, Shuaiwen Leon Song, Caiwen Ding, Xiaoye S. Li, Hang Liu:
Dr. Top-k: Delegate-Centric Top-k on GPUs. CoRR abs/2109.08219 (2021) - [i12]Shaoyi Huang, Dongkuan Xu, Ian En-Hsu Yen, Sung-En Chang, Bingbing Li, Shiyang Chen, Mimi Xie, Hang Liu, Caiwen Ding:
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm. CoRR abs/2110.08190 (2021) - [i11]Bingbing Li, Hongwu Peng, Rajat Sainju, Junhuan Yang, Lei Yang, Yueying Liang, Weiwen Jiang, Binghui Wang, Hang Liu, Caiwen Ding:
Detecting Gender Bias in Transformer-based Models: A Case Study on BERT. CoRR abs/2110.15733 (2021) - 2020
- [j2]Bolong Zheng, Xi Zhao, Lianggui Weng, Nguyen Quoc Viet Hung, Hang Liu, Christian S. Jensen:
PM-LSH: A Fast and Accurate LSH Framework for High-Dimensional Approximate NN Search. Proc. VLDB Endow. 13(5): 643-655 (2020) - [c29]Runbin Shi, Yuhao Ding, Xuechao Wei, He Li, Hang Liu, Hayden Kwok-Hay So, Caiwen Ding:
FTDL: A Tailored FPGA-Overlay for Deep Learning with High Scalability. DAC 2020: 1-6 - [c28]Bingbing Li, Zhenglun Kong, Tianyun Zhang, Ji Li, Zhengang Li, Hang Liu, Caiwen Ding:
Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning. EMNLP (Findings) 2020: 3187-3199 - [c27]Runbin Shi, Yuhao Ding, Xuechao Wei, Hang Liu, Hayden Kwok-Hay So, Caiwen Ding:
FTDL: An FPGA-tailored Architecture for Deep Learning Systems. FPGA 2020: 320 - [c26]Yuede Ji, Hang Liu, H. Howie Huang:
SWARMGRAPH: Analyzing Large-Scale In-Memory Graphs on GPUs. HPCC/DSS/SmartCity 2020: 52-59 - [c25]Linnan Wang, Wei Wu, Junyu Zhang, Hang Liu, George Bosilca, Maurice Herlihy, Rodrigo Fonseca:
FFT-based Gradient Sparsification for the Distributed Training of Deep Neural Networks. HPDC 2020: 113-124 - [c24]Md Hafizul Islam Chowdhuryy, Hang Liu, Fan Yao:
BranchSpec: Information Leakage Attacks Exploiting Speculative Branch Instruction Executions. ICCD 2020: 529-536 - [c23]Bingbing Li, Santosh Pandey, Haowen Fang, Yanjun Lyv, Ji Li, Jieyang Chen, Mimi Xie, Lipeng Wan, Hang Liu, Caiwen Ding:
FTRANS: energy-efficient acceleration of transformers using FPGA. ISLPED 2020: 175-180 - [c22]Shilong Wang, Da Li, Hengyong Yu, Hang Liu:
ELDA: LDA made efficient via algorithm-system codesign submission. PPoPP 2020: 407-408 - [c21]Santosh Pandey, Lingda Li, Adolfy Hoisie, Xiaoye S. Li, Hang Liu:
C-SAW: a framework for graph sampling and random walk on GPUs. SC 2020: 56 - [i10]Anil Gaihre, Xiaoye S. Li, Hang Liu:
GSoFa: Scalable Sparse LU Symbolic Factorization on GPUs. CoRR abs/2007.00840 (2020) - [i9]Bingbing Li, Santosh Pandey, Haowen Fang, Yanjun Lyv, Ji Li, Jieyang Chen, Mimi Xie, Lipeng Wan, Hang Liu, Caiwen Ding:
FTRANS: Energy-Efficient Acceleration of Transformers using FPGA. CoRR abs/2007.08563 (2020) - [i8]Shilong Wang, Hang Liu, Anil Gaihre, Hengyong Yu:
EZLDA: Efficient and Scalable LDA on GPUs. CoRR abs/2007.08725 (2020) - [i7]Yijue Wang, Chenghong Wang, Zigeng Wang, Shanglin Zhou, Hang Liu, Jinbo Bi, Caiwen Ding, Sanguthevar Rajasekaran:
MCMIA: Model Compression Against Membership Inference Attack in Deep Neural Networks. CoRR abs/2008.13578 (2020) - [i6]Yijue Wang, Jieren Deng, Dan Guo, Chenghong Wang, Xianrui Meng, Hang Liu, Caiwen Ding, Sanguthevar Rajasekaran:
SAPAG: A Self-Adaptive Privacy Attack From Gradients. CoRR abs/2009.06228 (2020) - [i5]Bingbing Li, Zhenglun Kong, Tianyun Zhang, Ji Li, Zhengang Li, Hang Liu, Caiwen Ding:
Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning. CoRR abs/2009.08065 (2020) - [i4]Santosh Pandey, Lingda Li, Adolfy Hoisie, Xiaoye S. Li, Hang Liu:
C-SAW: A Framework for Graph Sampling and Random Walk on GPUs. CoRR abs/2009.09103 (2020)
2010 – 2019
- 2019
- [c20]Anil Gaihre, Santosh Pandey, Hang Liu:
Deanonymizing Cryptocurrency With Graph Learning: The Promises and Challenges. CNS 2019: 1-3 - [c19]Eric Finnerty, Zachary Sherer, Hang Liu, Yan Luo:
Dr. BFS: Data Centric Breadth-First Search on FPGAs. DAC 2019: 208 - [c18]Zachary Sherer, Eric Finnerty, Yan Luo, Hang Liu:
Software Hardware Co-Optimized BFS on FPGAs. FPGA 2019: 190 - [c17]Anil Gaihre, Zhenlin Wu, Fan Yao, Hang Liu:
XBFS: eXploring Runtime Optimizations for Breadth-First Search on GPUs. HPDC 2019: 121-131 - [c16]Santosh Pandey, Xiaoye Sherry Li, Aydin Buluç, Jiejun Xu, Hang Liu:
H-INDEX: Hash-Indexing for Parallel Triangle Counting on GPUs. HPEC 2019: 1-7 - [c15]Bibek Bhattarai, Hang Liu, H. Howie Huang:
CECI: Compact Embedding Cluster Index for Scalable Subgraph Matching. SIGMOD Conference 2019: 1447-1462 - [c14]Hang Liu, H. Howie Huang:
SIMD-X: Programming and Processing of Graph Algorithms on GPUs. USENIX ATC 2019: 411-428 - [i3]Asma Aloufi, Peizhao Hu, Hang Liu, Sherman S. M. Chow:
Universal Location Referencing and Homomorphic Evaluation of Geospatial Query. IACR Cryptol. ePrint Arch. 2019: 820 (2019) - 2018
- [c13]Anil Gaihre, Yan Luo, Hang Liu:
Do Bitcoin Users Really Care About Anonymity? An Analysis of the Bitcoin Transaction Graph. IEEE BigData 2018: 1198-1207 - [c12]Nai Xia, Chen Tian, Yan Luo, Hang Liu, Xiaoliang Wang:
UKSM: Swift Memory Deduplication via Hierarchical and Adaptive Memory Region Distilling. FAST 2018: 325-340 - [c11]Yang Hu, Hang Liu, H. Howie Huang:
High-Performance Triangle Counting on GPUs. HPEC 2018: 1-5 - [c10]Yang Hu, Hang Liu, H. Howie Huang:
TriCore: parallel triangle counting on GPUs. SC 2018: 14:1-14:12 - [c9]Yuede Ji, Hang Liu, H. Howie Huang:
iSpan: parallel identification of strongly connected components with spanning trees. SC 2018: 58:1-58:12 - [i2]Linnan Wang, Wei Wu, Yiyang Zhao, Junyu Zhang, Hang Liu, George Bosilca, Jack J. Dongarra, Maurice Herlihy, Rodrigo Fonseca:
SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks. CoRR abs/1811.08596 (2018) - [i1]Hang Liu, H. Howie Huang:
SIMD-X: Programming and Processing of Graph Algorithms on GPUs. CoRR abs/1812.04070 (2018) - 2017
- [c8]Hang Liu, H. Howie Huang:
Graphene: Fine-Grained IO Management for Graph Computing. FAST 2017: 285-300 - 2016
- [j1]Rajat Mittal, Jung Hee Seo, Vijay Vedula, Young J. Choi, Hang Liu, H. Howie Huang, Saurabh Jain, Laurent Younes, Theodore Abraham, Richard T. George:
Computational modeling of cardiac hemodynamics: Current status and future outlook. J. Comput. Phys. 305: 1065-1082 (2016) - [c7]Hang Liu, H. Howie Huang, Yang Hu:
iBFS: Concurrent Breadth-First Search on GPUs. SIGMOD Conference 2016: 403-416 - 2015
- [c6]Hang Liu, H. Howie Huang:
Enterprise: breadth-first graph traversal on GPUs. SC 2015: 68:1-68:12 - 2014
- [c5]H. Howie Huang, Hang Liu:
Big data machine learning and graph analytics: Current state and future challenges. IEEE BigData 2014: 16-17 - [c4]Yu Xiang, Hang Liu, Tian Lan, H. Howie Huang, Suresh Subramaniam:
Optimizing job reliability via contention-free, distributed scheduling of vm checkpointing. DCC@SIGCOMM 2014: 59-64 - 2013
- [c3]Hang Liu, Jung Hee Seo, Rajat Mittal, H. Howie Huang:
GPU-accelerated scalable solver for banded linear systems. CLUSTER 2013: 1-8 - 2012
- [c2]Hang Liu, Jung Hee Seo, Rajat Mittal, H. Howie Huang:
Abstract: Matrix Decomposition Based Conjugate Gradient Solver for Poisson Equation. SC Companion 2012: 1499-1500 - [c1]Hang Liu, Jung Hee Seo, Rajat Mittal:
Poster: Matrix Decomposition Based Conjugate Gradient Solver for Poisson Equation. SC Companion 2012: 1501
Coauthor Index