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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.uni-trier.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
[j12]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)- [c52]Santosh Pandey, Amir Yazdanbakhsh, Hang Liu:
TAO: Re-Thinking DL-based Microarchitecture Simulation. SIGMETRICS/Performance (Abstracts) 2024: 23-24 - [i24]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) - [i23]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 - 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
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