©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 (R1 Doctoral Research University). He received his Ph.D. in Computer Science from University of California, Riverside in 2018 (supervised by Dr. Zizhong Chen and Dr. Franck Cappello) and his B.S. in Mathematics from University of Science and Technology of China in 2013. 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 system, big data analytics, scientific data management, fault tolerance and resilience, and large-scale machine learning. He has published in the top-tier HPC and big data conferences and journals, including IEEE BigData, Cluster, IPDPS, ACM ICS, HPDC, PPoPP, SC, IEEE TPDS, etc. He has been working closely with multiple teams teams and scientists from the DOE Argonne National Lab, Los Alamos National Lab, Brookhaven National Laboratory, Oak Ridge National Lab, and Pacific Northwest National Lab. His research has been supported by DOE, NOAA, and Xilinx.

According to CSRankings (2016 - 2019), Dr. Tao leads UA HPC research ranked #21 in the nation! More details about Dr. Tao can be found in his CV.

I am currently looking for outstanding Ph.D. students in the area of high-performance computing and big data analytics. Details can be found here. Please send me your CV and transcript if you are interested!

 

LATEST NEWS

  • 01/2020: I will serve as TPC for IEEE Cluster'20. Please consider a submission!

  • 01/2020: Our paper has been featured by HPCWire!

  • 01/2020: I will serve as Program Co-Chair for the 21st IEEE International Conference on Scalable Computing & Communications (ScalCom 2021).

  • 01/2020: I will serve as Research Posters Committee Member of ACM/IEEE SC'20

  • 12/2019: One paper has been accepted for publication in IEEE IPDPS'20. Congratulations to Sian!

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

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

  • 09/2019: I will serve as co-chair of CPSBigData 2019 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: One paper has been accepted for publication in ACM ICS'19. The acceptance rate is 23.3%.

  • 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 Argonne National Laboratory!

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

  • 01/2019: Two papers have 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!

 

RESEARCH INTERESTS

  • High-performance computing

  • Scientific data management, visualization, and analytics

  • Large-scale machine learning and deep learning

  • High-performance reconfigurable computing

  • Resilient, trustworthy, and energy-efficient computing

  • Adaptive algorithms, solvers, and optimization in scientific computing

  • Big data software stacks and ecosystems

 

SELECTED PUBLICATIONS

(with my students underlined)

IPDPS'20

Sian Jin, Pascal Grosset, Christopher M. Biwer, Jesus Pulido, Jiannan Tian, Dingwen Tao*, and James Ahrens.

Understanding GPU-Based Lossy Compression for Extreme-Scale Cosmological Simulations.

Proceedings of the 34th IEEE International Parallel and Distributed Symposium, New Orleans, LA, May 18 – 22, 2020. [ Preprint ]

PPOPP'20

Jiannan Tian, Sheng Di, Chengming Zhang, Xin Liang, Sian Jin, Dazhao Cheng, Dingwen Tao*, and Franck Cappello.

waveSZ: A Hardware-Algorithm Co-Design of Efficient Lossy Compression for Scientific Data.

Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, San Diego, California, USA, February 22 – 26, 2020. [ Preprint ]

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 ] [ Presentation ]

IEEE TPDS

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 ]

IJHPCA

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 ]

IEEE TPDS

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 ]

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: 22.7% (78/344) [ 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 ] [ Presentation ] [ Software ]

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 ]

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 ]

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. Acceptance Rate: 27.7% (39/141) [ 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 ] [ Presentation ]

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. Acceptance Rate: 0.65% (1/164) [ Paper ] [ Presentation ]

Best Paper Award

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. Acceptance Rate: 2.6% (4/154) [ Paper ] [ Presentation ]

Best Paper Award in Data, Storage, Visualization Area

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. Acceptance Rate: 31.8% (49/154) [ Paper ] [ Presentation ]

PRIOR TO UA

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 ][ Presentation ]

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 ] [ Presentation ]

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 ] [ Presentation ] [ Software ]

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 ] [ Presentation ]

 

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!