©2019 by Dingwen Tao

DINGWEN TAO

Assistant Professor

Department of Computer Science

The University of Alabama

Office: Bevill 1107

Email: tao@cs.ua.edu

Phone: +1 (205) 348-0062

 
dingwen-photo.jpg

BIOGRAPHY

Dr. Dingwen Tao is an assistant professor in the Department of Computer Science at The University of Alabama. He received his Ph.D. in Computer Science from University of California, Riverside in 2018, focusing on High-Performance Computing and his B.S. in 2013 in Mathematics from University of Science and Technology of China in 2013, majoring in Information and Computing Science. Prior to joining UA, he worked as a R&D intern in the Computational Science Initiative Division at Brookhaven National Laboratory, Mathematics and Computer Science Division at Argonne National Laboratory, and High-Performance Computing Group at Pacific Northwest National Laboratory.

Dr. Dingwen Tao's current research interests include High-Performance Computing (HPC), Parallel and Distributed Computing, Big Data Analytics, Scientific Data Analysis and Reduction, Extreme-Scale Fault Tolerance and Resilience, and Large-scale Machine Learning. He has published in the major HPC and Big Data related conferences and journals, including IEEE BigData/CLUSTER/IPDPS, ACM ICS/HPDC, ACM/IEEE SC, IEEE TPDS, IJHPCA, etc. He has been closely working with many teams and scientists from Argonne National Laboratory (ANL), Los Alamos National Laboratory (LANL), Brookhaven National Laboratory (BNL), and Pacific Northwest National Laboratory (PNNL). His research has been supported by U.S. Department of Energy (DOE), National Oceanic and Atmospheric Administration (NOAA), and Xilinx.

The University of Alabama achieved Doctoral Universities – Very High Research Activity status (formerly known as the R1 category) in the Carnegie Classification of Institutions of Higher Education update released in December, 2018. This is the nation’s highest level of research activity for institutions that grant doctoral degrees. Research funding, research staff and the number of doctoral graduates are among the criteria used in determining Carnegie classifications.

Dr. Tao is affiliated with Alabama Water Institute (AWI), Remote Sensing Center (RSC), and Center for Complex Research. More details about me can be found in my CV or NSF-style Biosketch.

According to CSRankings (2016 - 2019), Dr. Tao leads UA HPC research ranked #21 in the nation!

 

LATEST NEWS

  • 11/2019: One paper has been accepted for publication in ACM PPoPP'20. Congratulations to Jiannan!

  • 11/2019: Invited talks at Northeastern University and Boston University. [Flyer]

  • 10/2019: Two papers have been accepted for publication in IEEE BigData'19.

  • 09/2019: We organize CPSBigData Workshop colocated with IEEE IGSC Conference. Please consider to submit.

  • 09/2019: The source code of TSM2 (ICS'19) has been released in our GitHub

  • 06/2019: One paper has been accepted for publication in IEEE/ACM SC'19.

  • 06/2019: A one-year grant ($250k) is awarded for "Center for Remote Sensing of Snow and Soil Moisture" (co-PI: Dingwen Tao) by NOAA.

  • 06/2019: A grant ($25k) is awarded for "Improving Lossy Compression for Scientific Applications at Extreme Scale" (PI: Dingwen Tao) by Argonne National Lab.

  • 04/2019: Welcome Cody to join HiPDAC lab!

  • 04/2019: One paper has been accepted for publication in ACM ICS'19. The acceptance rate is 23.3%.

  • 04/2019: Welcome Cody to join HiPDAC lab!

  • 03/2019: One paper has been accepted for publication in ACM HPDC'19. Congratulations to Sian!

  • 03/2019: I will serve on the technical committee member for IEEE HiPC'19.

  • 02/2019: Congratulations to Jiannan Tian for the summer internship offer from National Water Center!

  • 02/2019: Congratulations to Sian Jin for the summer internship offer from Los Alamos National Laboratory!

  • 02/2019: One paper has been accepted for publication in IEEE MSST'19. The acceptance rate is 29.6%.

  • 01/2019: Welcome Jiannan to join HiPDAC lab!

  • 01/2019: One paper has been accepted for publication in IEEE TPDS.

  • 11/2018: We got a $4,000 hardware and software donation from Xilinx, Inc. Many thanks to Xilinx for supporting our research!

  • 11/2018: We got a funding support to attend which will be held on December 3-4, 2018 at Berkeley Lab.

 

RESEARCH INTERESTS

  • High-Performance Computing

  • Scientific Data Management, Visualization, and Analytics

  • Machine Learning and Deep Learning for HPC

  • Adaptive Algorithms, Solvers, and Optimization for HPC

  • Extreme-scale Scientific Simulations

  • Big Data Analytics in Remote Sensing

  • Fault Tolerance and Resilience Techniques for HPC

  • Energy-Efficient Algorithm and Software

  • Big Data Software Stacks and Ecosystems

 

SELECTED PUBLICATIONS

(with my students underlined)

BIGDATA'19

Yuqi Fu, Shaolun Zhang, Jose Terrero, Ying Mao, Guangya Liu, Sheng Li, and Dingwen Tao

Progress-based Container Scheduling for Short-lived Applications in a Kubernetes Cluster.

Proceedings of 2019 IEEE International Conference on Big Data, Los Angeles, CA, USA, December 9 - 12, 2019. Acceptance Rate: 19.3% (106/550). [Preprint]

BIGDATA'19

Donglin Yang, Wei Rang, Dazhao Cheng, Yu Wang, Jiannan Tian, and Dingwen Tao

Elastic Executor Provisioning for Iterative Workloads on Apache Spark.

Proceedings of 2019 IEEE International Conference on Big Data, Los Angeles, CA, USA, December 9 - 12, 2019. Acceptance Rate: 19.3% (106/550). [Preprint]

SC'19

Xin Liang, Sheng Di, Sihuan Li, Dingwen Tao, Bogdan Nicolae, Zizhong Chen, and Franck Cappello.
Significantly Improving Lossy Compression Quality based on An Optimized Hybrid Prediction Model.

Proceedings of the 31st ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, Denver, Colorado, USA, Nov 17 - 22, 2019. Acceptance Rate: 25.3% (87/344) [ Preprint ]

CLUSTER'19

Xin Liang, Sheng Di, Dingwen Tao, Sihuan Li, Bogdan Nicolae, Zizhong Chen, and Franck Cappello.

Improving Performance of Data Dumping with Lossy Compression for Scientific Simulation.

Proceedings of the 2019 IEEE International Conference on Cluster Computing, Albuquerque, New Mexico USA, September 23 - 26, 2019. [ Preprint ]

IJHPCA'19

Franck Cappello, Sheng Di, Sihuan Li, Xin Liang, Ali Murat Gok, Dingwen Tao, Chun Hong Yoon, Xin-Chuan Wu, Yuri Alexeev, Frederic T Chong.
Use Cases of Lossy Compression for Floating-Point Data in Scientific Data Sets.
The International Journal of High Performance Computing Applications. [ Paper ]

ICS'19

Jieyang Chen, Nan Xiong, Xin Liang, Dingwen Tao, Sihuan Li, Kaiming Ouyang, Kai Zhao, Nathan DeBardeleben, Qiang Guan, and Zizhong Chen.

TSM2: Optimizing Tall-and-Skinny Matrix-Matrix Multiplication on GPUs.

Proceedings of the 33rd ACM International Conference on Supercomputing, Phoenix, AZ, USA, June 26 - 28, 2019. Acceptance Rate: 23.3% (45/193) [ Paper ] [ Slides ] [Software]

HPDC'19

Sian Jin, Sheng Di, Xin Liang, Jiannan Tian, Dingwen Tao, and Franck Cappello.

DeepSZ: A Novel Framework to Compress Deep Neural Networks by Using Error-Bounded Lossy Compression.

Proceedings of the 28th ACM International Symposium on High-Performance Parallel and Distributed Computing, Phoenix, AZ, USA, June 24 - 28, 2019. Acceptance Rate: 20.7% (22/106) [ Paper ] [ Slides ]

MSST'19

Xiangyu Zou, Tao Lu, Wen Xia, Xuan Wang, Weizhe Zhang, Sheng Di, Dingwen Tao, and Franck Cappello.

Accelerating Relative-error Bounded Lossy Compression for HPC datasets with Precomputation-Based Mechanisms.

Proceedings of the 35th IEEE Symposium on Mass Storage Systems and Technologies, Santa Clara, CA, USA, May 20 - 24, 2019. Acceptance Rate: 29.6% (21/71) [ Paper ] [ Slides ]

TPDS'19

Dingwen Tao, Sheng Di, Xin Liang, Zizhong Chen, and Franck Cappello.

Optimizing Lossy Compression Rate-Distortion from Automatic Online Selection between SZ and ZFP.

IEEE Transactions on Parallel and Distributed Systems. [ Paper ]

BIGDATA'18

Xin Liang, Sheng Di, Dingwen Tao, Sihuan Li, Shaomeng Li, Hanqi Guo, Zizhong Chen, and Franck Cappello.

Error-Controlled Lossy Compression Optimized for High Compression Ratios of Scientific Datasets.

Proceedings of the 2018 IEEE International Conference on Big Data, Seattle, WA, USA, December 10 - 13, 2018. Acceptance Rate: 18.9% (98/518) [ Paper ] [ Slides ]

TPDS'18

Sheng Di, Dingwen Tao, Xin Liang, and Franck Cappello.

Efficient Lossy Compression for Scientific Data based on Pointwise Relative Error Bound.

IEEE Transactions on Parallel and Distributed Systems. [ Paper ]

CLUSTER'18

Dingwen Tao, Sheng Di, Xin Liang, Zizhong Chen, and Franck Cappello.

Fixed-PSNR Lossy Compression for Scientific Data.

Proceedings of the 2018 IEEE International Conference on Cluster Computing, Belfast, UK, September 10 - 13, 2018. [ Paper ] [ Slides ]

CLUSTER'18

Xin Liang, Sheng Di, Dingwen Tao, Zizhong Chen, and Franck Cappello.

An Efficient Transformation Scheme for Lossy Data Compression with Point-wise Relative Error Bound.

Proceedings of the 2018 IEEE International Conference on Cluster Computing, Belfast, UK, September 10 - 13, 2018. [ Paper ] [ Slides ]

Best Paper Award in Data, Storage, Visualization Area

CLUSTER'18

Ali Murat Gok, Sheng Di, Yuri Alexeev, Dingwen Tao, Vladimir Mironov, Xin Liang, and Franck Cappello.

PaSTRI: Error-Bounded Lossy Compression for Two-Electron Integrals in Quantum Chemistry.

Proceedings of the 2018 IEEE International Conference on Cluster Computing, Belfast, UK, September 10 - 13, 2018. [ Paper ] [ Slides ]

Overall Best Paper Award

SC'18

Jieyang Chen, Hongbo Li, Sihuan Li, Xin Liang, Panruo Wu, Dingwen Tao, Kaiming Ouyang, Yuanlai Liu, Qiang Guan, and Zizhong Chen.

FT-MAGMA: Fault Tolerance Dense Matrix Decomposition on Heterogeneous Systems with GPUs.

Proceedings of the 30th ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, Dallas, Texas, USA, Nov 11 - 16, 2018. Acceptance Rate: 19.1% (55/288) [ Paper ]

HPDC'18

Dingwen Tao, Sheng Di, Xin Liang, Zizhong Chen, and Franck Cappello.

Improving Performance of Iterative Methods by Lossy Checkpointing.

Proceedings of the 27th ACM International Symposium on High-Performance Parallel and Distributed Computing, Tempe, AZ, USA, June 11 - 15, 2018. Acceptance Rate: 19.6% (22/112) [ Paper ][ Slides ][ Lightning Talk ]

BIGDATA'17

Dingwen Tao, Sheng Di, Zizhong Chen, and Franck Cappello. In-Depth

Exploration of Single-Snapshot Lossy Compression Techniques for N-Body Simulations.

Proceedings of the 2017 IEEE International Conference on Big Data, Boston, MA, USA, December 11 - 14, 2017. Acceptance Rate: 19.9% (87/437) [ Paper ] [ Slides ]

IJHPCA'17

Dingwen Tao, Sheng Di, Hanqi Guo, Zizhong Chen, and Franck Cappello.

Z-checker: A Framework for Assessing Lossy Compression of Scientific Data.

The International Journal of High Performance Computing Applications. [ Paper ] [ Software ]

SC'17

Xin Liang, Jieyang Chen, Dingwen Tao, Sihuan Li, Panruo Wu, Hongbo Li, Kaiming Ouyang, Yuanlai Liu, Fengguang Song, and Zizhong Chen.

Correcting Soft Errors Online in Fast Fourier Transform.

Proceedings of the 29th ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, Denver, Colorado, USA, Nov 12 - 17, 2017. Acceptance Rate: 18.6% (61/327). [ Paper ]

IPDPS'17

Dingwen Tao, Sheng Di, Zizhong Chen, and Franck Cappello.

Significantly Improving Lossy Compression for Scientific Data Sets Based on Multidimensional Prediction and Error-Controlled Quantization.

Proceedings of the 31st IEEE International Parallel & Distributed Processing Symposium, Orlando, Florida, USA, May 29 - June 2, 2017. Acceptance Rate: 22.8% (116/508). [ Paper ] [ Slides ] [ Software ]

PPOPP'17

Panruo Wu, Nathan, Debardeleben, Qiang Guan, Sean Blanchard, Jieyang Chen, Dingwen Tao, Xin Liang, Ouyang Kaiming, Sihuan Li, and Zizhong Chen.

Silent Data Corruption Resilient Two-sided Matrix Factorizations.

Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Austin, Texas, USA, February 4 - 8 2017. Acceptance Rate: 21.9%. 29/132) [ Paper ]

SC'16

Jieyang Chen, Li Tan, Panruo Wu, Dingwen Tao, Hongbo Li, Xin Liang, Sihuan Li, Rong Ge, Laxmi Bhuyan, and Zizhong Chen.

GreenLA: Green Linear Algebra Software for GPU-Accelerated Heterogeneous Computing.

Proceedings of the 28th ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, Salt Lake City, Utah, USA, Nov 13 - 18, 20 16. Acceptance Rate: 18.4% (82/446). [ Paper ]

HPDC'16

Dingwen Tao, Shuaiwen Leon Song, Sriram Krishnamoorthy, Panruo Wu, Xin Liang, Zheng Eddy Zhang, Darren Kerbyson, and Zizhong Chen.

New-Sum: A Novel Online ABFT Scheme for General Iterative Methods.

Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing, Kyoto, JAPAN, May 31- June 4, 2016 Acceptance Rate: 15.5% (20/129). [ Paper ] [ Slides ]

HPDC'16

Panruo Wu, Qiang Guan, Nathan DeBardeleben, Sean Blanchard, Dingwen Tao, Xin Liang, Jieyang Chen, and Zizhong Chen.

Towards Practical Algorithm Based Fault Tolerance in Dense Linear Algebra.

Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing, Kyoto, JAPAN, May 31 - June 4, 2016. Acceptance Rate: 15.5% (20/129). [ Paper ]

 

COURSES

GRADUATE COURSE

CS 570: Computer Algorithms, Fall 2019 (UA)
CS 591: High Performance Computing, Spring 2019 (UA)
CS 211: "High Performance Computing", Fall 2014, Fall 2015 & Fall 2016 (UCR)

UNDERGRADUATE COURSE

CS 470: Computer Algorithms, Fall 2019 (UA)

CS 491: High Performance Computing, Spring 2019 (UA)
CS 012: "Intro to CS for SCI, MATH & ENGR II", Winter 2015 (UCR)

CS 008: "Introduction to Computing", Fall 2014 (UCR)

CS 006: "Effective Use of the World Wide Web", Fall 2014 (UCR)

 

WELCOME TO VISIT!