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DINGWEN TAO

Associate Professor in Intelligent Systems Engineering

Luddy School of Informatics, Computing, & Engineering

Indiana University Bloomington

Office: Luddy Hall 4124

Email: ditao@iu.edu

Phone: +1 (812) 855-2822

 
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BIOGRAPHY

Dr. Dingwen Tao is an Associate Professor in the Luddy School of Informatics, Computing, and Engineering at Indiana University Bloomington. He received his Ph.D. in Computer Science from University of California, Riverside in 2018 and his B.S. in Mathematics from University of Science and Technology of China in 2013.


Dr. Dingwen Tao worked as an Assistant Professor at Washington State University and University of Alabama between 2018~2022. Prior to that, he worked in the Computational Science Initiative at Brookhaven National Laboratory, the Mathematics and Computer Science Division at Argonne National Laboratory, and the High-Performance Computing Group at Pacific Northwest National Laboratory.

Dr. Dingwen Tao is the recipient of the Amazon Research Award (2022)Meta Research Award (2022),  R&D100 Awards Winner (2021), IEEE Computer Society TCHPC Early Career Researchers Award for Excellence in High Performance Computing (2020),  NSF CISE Research Initiation Initiative (CRII) Award (2020), IEEE CLUSTER Best Paper Award (2018), UCR Dissertation Year Program (DYP) Award (2017).

Dr. Dingwen Tao's 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 SC, ICS, HPDC, PPoPP, DAC, PACT, IPDPS, CLUSTER, ICPP, BigData, IEEE TC, IEEE TPDS, etc. He has been working closely with multiple teams teams and scientists from the 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 NSF, DOE, NOAA, AMD and Xilinx. For more details, please refer to his CV (EnglishChinese).

I am always looking for outstanding Ph.D. and undergraduate students who are interested in the area of HPC, computing systems, data analytics, and machine learning. Details can be found here. Please send me your CV and transcript to me.

 

LATEST NEWS

  • 12/2022: I receive the NSF CAREER Award! It is the NSF's most prestigious award in support of early-career faculty. Thanks a lot to NSF! [ Abstract ]

  • 11/2022: One paper has been accepted for publication in AAAI'23.

  • 11/2022: One paper has been accepted for publication in VLDB'23.

  • 11/2022: One paper has been accepted for publication in PPoPP'23.

  • 10/2022: Sian and Boyuan received SC'22 Student Travel Grants. Jiannan and Baixi received ACM SIGHPC Student Research Competition Travel Grants.

  • 09/2022: We received the 2022 Meta Research Award on AI System Hardware/Software Codesign! Many thanks to Meta Research[ Meta News ] [ IU News ]

  • 08/2022: Our two proposals were selected as a finalist in Meta RFP - Networking for AI and AI System Hardware/Software Codesign. 

  • 08/2022: One paper has been accepted for publication in PACT'22.

  • 07/2022: We presented our CSSI and CDS&E projects at 2022 NSF Cyberinfrastructure for Sustained Scientific Innovation (CSSI) Principal Investigator Meeting at  Alexandria, VA. [ CSSI-Poster ] [ CDS&E-Poster ]

  • 07/2022: A 250k OAC Core project was funded by NSF (my role: site PI). Thanks a lot to NSF! 

  • 07/2022: I gave a talk on compression software fosr HPC storage and I/O at Watson Software Paper Bar. Thank you for Yuxin's invitation! It was fun! [ Slides ] [ Thank You Letter ]

  • 07/2022: I gave a 3-hour talk at OxML Summer School Fundamental Sessions about ML Systems. [ Video ] [ Slides ]

  • 06/2022: One paper has been accepted for publication in FPL'22.

  • 06/2022: One paper has been accepted for publication in SC'22. Congratulations to Sian!

  • 04/2022: Two papers have been accepted for publication in ICS'22.

  • 04/2022: Two papers have been accepted for publication in HPDC'22.

  • 03/2022: One paper has been accepted for publication in ICDE'22.

  • 03/2022: I'll deliver a lecture at Oxford Machine Learning Summer School (OxML) for an ML Fundamentals module on June 27-29. Thanks for Yaodong's invitation!

  • 02/2022: Congratulations to Cody being admitted by UIUC and Princeton CS PhD program!

  • 02/2022: Our research has been featured again in the school's newsletter. [ News ]

  • 02/2022: Chengming got a research internship offer from Microsoft Research to work with a DeepSpeed team on building massive-scale AI systems.

  • 01/2022: Sian has been selected for PNNL-WSU Distinguished Graduate Research Program (DGRP) Fellowship. 

  • 12/2021: One paper has been accepted for publication in IEEE IPDPS'22

  • 11/2021: I joined the Editorial Board of IEEE Transactions on Parallel & Distributed Systems (TPDS).

  • 11/2021: Our cuSZ+ work has been selected as the 3rd Place Winner of the ACM Student Research Competition at SC'21! 

  • 11/2021: One paper has been accepted for publication in VLDB'22

  • 11/2021: Our project and award has been featured by WSU Insider!

  • 10/2021: Our SZ compression software has been selected for 2021 R&D100 Awards! [ News ]

  • 08/2021: A 280k CSSI project was funded by NSF (my role: site PI). Thanks a lot to NSF! 

  • 07/2021: A 25k project for "FAIR Surrogate Benchmarks Supporting AI and Simulation Research" is sub-awarded by Argonne National Lab (my role: single PI). Thanks a lot to ANL! 

  • More [...]

 

RESEARCH INTERESTS

Include but not limited to:

  • High-performance computing

  • Scientific data compression

  • GPU and FPGA acceleration

  • Computational storage

  • Cloud computing (e.g., Cloud HPC)

  • Distributed machine/deep learning

  • Numerical algorithms and simulations

  • Quantum computing

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SELECTED GRANTS

  • NSF, Site PI, OAC Core: CEAPA: A Systematic Approach to Minimize Compression Error Propagation in HPC Applications, $250K, 2022-2025. [ Abstract ]

  • NSF, Site PI, CSSI: Elements: ROCCI: Integrated Cyberinfrastructure for In Situ Lossy Compression Optimization Based on Post Hoc Analysis Requirements, $280K, 2021-2024. [ Abstract ]

  • NSF, Lead PI, CDS&E: HyLoC: Objective-driven Adaptive Hybrid Lossy Compression Framework for Extreme-Scale Scientific Applications, $270K, 2020-2023. [ Abstract ]

  • NSF, Single PI, CRII: OAC: An Efficient Lossy Compression Framework for Reducing Memory Footprint for Extreme-Scale Deep Learning on GPU-Based HPC Systems, $185K, 2020-2022. [ Abstract ]

  • NSF, co-PI, CC* Compute: Accelerating Advances in Science and Engineering at The University of Alabama Through HPC Infrastructure, $400K, 2020-2022. [ Abstract ]

 

AWARDS

  • ​​Amazon Research Award (ARA), 2022

  • Meta Research Award, 2022

  • R&D 100 Awards Winner, 2021

  • IEEE TCHPC Early Career Researchers Award for Excellence in HPC, 2020

  • NSF CISE Research Initiation Initiative (CRII) Award, 2020

  • IEEE Cluster Best Paper Award, 2018

  • UCR Dissertation Year Program (DYP) Award, 2017

  • UCR Dean’s Distinguished Fellowship, 2013

 

SELECTED PUBLICATIONS

 

SOFTWARE

If you are not familiar with compression, there is a show called Silcon Valley (TV series), which will give you an idea how compression evolves our lives. Highly recommended!​

 

TEACHING

UNDERGRADUATE COURSE

  • WSU CS 233 - Advanced Data Structures [Spring 2021, Fall 2021]: This course introduces data structures, beyond what was already covered in the introductory data structures class CS 132, as well as algorithm design using different data structures and algorithmic techniques.

  • WSU CS/EE 455 - Introduction to Computer Networks [Fall 2020, Fall 2021]: This course introduces concepts and implementations of computer networks, architectures, protocol layers, internetworking and addressing case studies.

  • UA CS 470 - Computer Algorithms [Fall 2019]: This course introduces construction of efficient algorithms, i.e., divide-and-conquer, dynamic programming, greedy method, max flow and matchings, parallel algorithms, string matching, computational geometry, NP-completeness.

  • UA CS 491 - High Performance Computing [Spring 2019]: This course introduces knowledge and fundamental concepts of high performance computing as well as hands-on experience of the core technology in the field. The objective of this class is to understand how to achieve high performance on a wide range of computational platforms. 

GRADUATE COURSE

  • IU ENGR 516 - Engineering Cloud Computing [Fall 2022, Spring 2023]: This course covers basic concepts on programming models and tools of cloud computing to support data intensive science applications. Students will get to know the latest research topics of cloud platforms, parallel algorithms, storage and high level language for proficiency with a complex ecosystem of tools that span many disciplines.

  • WSU CS 580 - Advanced Topics in Computer Science [Spring 2022]: This course introduces data reduction techniques and tools for scientific applications, including a series of introductory lectures on classic compression algorithms, dimensionality reduction methods, and emerging scientific data compressors, as well as design and use of data quality assessment methods and tools in real-world applications.

  • UA CS 570 - Computer Algorithms [Fall 2019]: This course introduces construction of efficient algorithms, i.e., divide-and-conquer, dynamic programming, greedy method, max flow and matchings, parallel algorithms, string matching, computational geometry, NP-completeness.

  • UA CS 591 - High Performance Computing [Spring 2019]: This course introduces knowledge and fundamental concepts of high performance computing as well as hands-on experience of the core technology in the field.

 

SELECTED PUBLICATIONS

(with my students underlined, *corresponding author)

top-cs: top-tier Computer Science publication venue according to CSRankings

top-cr: top-tier Computer Science publication venue according to Conference Ranks

AAAI'23

top-cs, top-cr

Jinqi Xiao*, Chengming Zhang*, Yu Gong, Miao Yin, Yang Sui, Lizhi Xiang, Dingwen Tao, Bo Yuan.

HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks.

Proceedings of the 37th AAAI Conference on Artificial Intelligence, Washington DC, USA, February 7-14, 2023. Acceptance Rate: 19% (1721/8777)  [ Paper ] (*equal contribution)

VLDB'23

top-cs, top-cr

Pu Jiao, Sheng Di, Hanqi Guo, Kai Zhao, Jiannan Tian, Dingwen Tao, Xin Liang*, Franck Cappello.

Toward Quantity-of-Interest Preserving Lossy Compression for Scientific Data.

Proceedings of the 49th International Conference on Very Large Data Bases, Vancouver, Canada, August 28-September 1, 2023.  [ Paper ]

PPOPP'23

top-cr

Lizhi Xiang*, Miao Yin*, Chengming Zhang, Aravind Sukumaran-Rajam, P. Saday, Bo Yuan, Dingwen Tao.

TDC: Towards Extremely Efficient CNNs on GPUs via Hardware-Aware Tucker Decomposition.

Proceedings of the 28th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Montreal, Canada, February 25 - March 1, 2023. Acceptance Rate: 23% (31/131) [ Paper ] (*equal contribution)

SC'22

top-cs, top-cr

Sian Jin, Dingwen Tao*, Houjun Tang, Sheng Di, Suren Byna, Zarija Lukic, and Franck Cappello.

Accelerating Parallel Write via Deeply Integrating Predictive Lossy Compression with HDF5.

Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC), Dallas, TX, USA, Nov 13-18, 2022. Acceptance Rate: 24% (81/330) [ Paper ] [ Slides ] [ Talk ]

PACT'22

top-cr

Xinyu Chen, Marco Minutoli, Jiannan Tian, Mahantesh Halappanavar, Ananth Kalyanaraman, and Dingwen Tao*.

HBMax: Optimizing Memory Efficiency for Parallel Influence Maximization on Multicore Architectures.

Proceedings of the 31st ACM International Conference on Parallel Architectures and Compilation Techniques, Chicago, IL, USA, October 10-12, 2022.  [ Paper ] [ Slides ] [ Poster ]

VLDB'22

top-cs, top-cr

Sian Jin, Chengming Zhang, Xintong Jiang, Yunhe Feng, Hui Guan, Guanpeng Li, Shuaiwen Leon Song, Dingwen Tao*.

COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression.

Proceedings of the 48th International Conference on Very Large Data Bases, Sydney, Australia, September 5-9, 2022.  [ Paper ] [ Slides ] [ Poster ] [ Talk ]

TPDS'22

top journal, impact factor: 4.181

Yuanjian Liu, Sheng Di, Kai Zhao, Sian Jin, Cheng Wang, Kyle Chard, Dingwen Tao, Ian Foster, and Franck Cappello.

Optimizing Error-Bounded Lossy Compression for Scientific Data with Diverse Constraints.

IEEE Transactions on Parallel and Distributed Systems. [ Paper ]

FPL'22

top-cr

Chengming Zhang, Tong Geng, Anqi Guo, Jiannan Tian, Martin Herbordt, Ang Li and Dingwen Tao*.

H-GCN: A Graph Convolutional Network Accelerator on Versal ACAP Architecture.

The 32nd International Conference on Field Programmable Logic and Applications, Belfast, UK, August 29-September 2, 2022. Acceptance Rate: 25% (33/129) [ Paper ] [ Slides ] { Talk ]

ICS'22

top-cs, top-cr

Chengming Zhang, Sian Jin, Tong Geng, Jiannan Tian, Ang Li, and Dingwen Tao*.

CEAZ: Accelerating Parallel I/O via Hardware-Algorithm Co-Designed Adaptive Lossy Compression.

Proceedings of the 36th ACM International Conference on Supercomputing, Virtual Event, USA, June 27-30, 2022. Acceptance Rate: 23% (39/165) [ Paper ] [ Slides ] [ Talk ]

ICS'22

top-cs, top-cr

Heng Zhang, Lingda Li, Hang Liu, Donglin Zhuang, Rui Liu, Chengying Huan, Shuang Song, Dingwen Tao, Yongchao Liu, Charles He, Yanjun Wu, and Shuaiwen Leon Song.

Bring Orders into Uncertainty: Enabling Efficient Uncertain Graph Processing via Novel Path Sampling on Multi-Accelerator System.

Proceedings of the 36th ACM International Conference on Supercomputing, Virtual Event, USA, June 27-30, 2022. Acceptance Rate: 23% (39/165) [ Paper ] [ Talk ]

HPDC'22

top-cs, top-cr

Daoce Wang, Jesus Pulido, Pascal Grosset, Sian JinJiannan Tian, James Ahrens, and Dingwen Tao*.

TAC: Optimizing Error-Bounded Lossy Compression for Three-Dimensional Adaptive Mesh Refinement Simulations.

Proceedings of the 31st ACM International Symposium on High-Performance Parallel and Distributed Computing, Minneapolis, Minnesota, June 27-July 1, 2022. Acceptance Rate: 19% (21/108). [ Paper ] [ Slides ] [ Talk ]

HPDC'22

top-cs, top-cr

Xiaodong Yu, Sheng Di, Kai Zhao, Jiannan Tian, Dingwen Tao, Xin Liang, and Franck Cappello.

Ultra-fast Error-bounded Lossy Compression for Scientific Dataset.

Proceedings of the 31st ACM International Symposium on High-Performance Parallel and Distributed Computing, Minneapolis, Minnesota, June 27-July 1, 2022. Acceptance Rate: 19% (21/108). [ Paper ]

ICDE'22

top-cs, top-cr

Sian Jin, Sheng Di, Jiannan Tian, Suren Byna, Dingwen Tao*, and Franck Cappello.

Improving Prediction-Based Lossy Compression Dramatically via Ratio-Quality Modeling.

Proceedings of the 38th IEEE International Conference on Data Engineering, Virtual Event, May 9-12, 2022. Acceptance Rate: 27% (211/780) [ Paper ] [ Slides ] [ Talk ]

IPDPS'22

top-cr

Cody Rivera, Sheng Di, Xiaoding Yu, Jiannan Tian, Dingwen Tao*, and Franck Cappello.

Optimizing Huffman Decoding for Error-Bounded Lossy Compression on GPUs.

Proceedings of the 36th IEEE International Parallel and Distributed Processing Symposium, Lyon, France, May 30-June 3, 2022. Acceptance Rate: 25% (123/474) [ Paper ] [ Slides ]

TC'21

top journal, impact factor: 3.131

Xin Liang, Ben Whitney, Jieyang Chen, Lipeng Wan, Qing Liu, Dingwen Tao, James Kress, Dave Pugmire, Matthew Wolf, Nobert Podhorszki, and Scott Klasky.

MGARD+: Optimizing Multilevel Methods for Error-bounded Scientific Data Reduction.

IEEE Transactions on Computers 71, no. 7 (2021): 1522-1536. [ Paper ]

CLUSTER'21

top-cr

Jiannan Tian, Sheng Di, Xiaodong Yu, Cody Rivera, Kai Zhao, Sian Jin, Yunhe Feng, Xin Liang, Dingwen Tao*, Franck Cappello.

Optimizing Error-Bounded Lossy Compression for Scientific Data on GPUs. Proceedings of the 2021 IEEE International Conference on Cluster Computing, Portland, OR, USA, September 7-10, 2021. Acceptance Rate: 29% (48/163) [ Paper ] [ Slides

ACM Student Research Competition 3rd Place Winner

CLUSTER'21

top-cr

Bo Fang, Daoce Wang, Sian Jin, Quincey Koziol, Zhao Zhang, Qiang Guan, Suren Byna, Sriram Krishnamoorthy, Dingwen Tao* (1st/2nd authors contributed equally).

Characterizing Impacts of Storage Faults on HPC Applications: A Methodology and Insights. Proceedings of the 2021 IEEE International Conference on Cluster Computing, Portland, OR, USA, September 7-10, 2021. Acceptance Rate: 29% (48/163) [ Paper ] [ Slides ]

ICS'21

top-cs, top-cr

Chengming Zhang, Geng Yuan, Wei Niu, Jiannan Tian, Sian Jin, Donglin Zhuang, Zhe Jiang, Yanzhi Wang, Bin Ren, Shuaiwen Leon Song, and Dingwen Tao*.

ClickTrain: Efficient and Accurate End-to-End Deep Learning Training via Fine-Grained Architecture-Preserving Pruning.

Proceedings of the 35th ACM International Conference on Supercomputing, Virtual Event, USA, June 14-17, 2021. Acceptance Rate: 24% (38/157) [ Paper ] [ Slides ]

HPDC'21

top-cs, top-cr

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

Adaptive Configuration of In Situ Lossy Compression for Cosmology Simulations via Fine-Grained Rate-Quality Modeling.

Proceedings of the 30th ACM International Symposium on High-Performance Parallel and Distributed Computing, Virtual Event, Sweden, June 21-25, 2021. Acceptance Rate: 21% (19/92). [ Paper ] [ Slides ]

IPDPS'21

top-cr

Jiannan Tian, Cody Rivera, Sheng Di, Jieyang Chen, Xin Liang, Dingwen Tao*, and Franck Cappello.

Revisiting Huffman Coding: Toward Extreme Performance on Modern GPU Architectures.

Proceedings of the 35th IEEE International Parallel and Distributed Processing Symposium, Portland, Oregon, May 17-21, 2021. Acceptance Rate: 22% (105/462) [ Paper ] [ Slides ] [ Talk ]

JPDC'21

impact factor: 2.296

Cody Rivera, Jieyang Chen, Nan Xiong, Jing Zhang, Shuaiwen Leong Song, and Dingwen Tao*.

TSM2X: High-Performance Tall-and-Skinny Matrix-Matrix Multiplication on GPUs.

Journal of Parallel and Distributed Computing 151 (2021): 70-85. [ Paper ] [ Code

PACT'20

top-cr

Jiannan Tian, Sheng Di, Kai Zhao, Cody Rivera, Megan Hickman, Robert Underwood, Sian Jin, Xin Liang, Jon Calhoun, Dingwen Tao*, and Franck Cappello.

cuSZ: An Efficient GPU Based Error-Bounded Lossy Compression Framework for Scientific Data.

Proceedings of the 29th ACM International Conference on Parallel Architectures and Compilation Techniques, Atlanta, GA, USA, October 3-7, 2020. Acceptance Rate: 25% (35/137)  [ Paper ] [ Slides ] [ Talk​ ] [ Code ]

ICPP'20

top-cr

Zhenbo Hu, Xiangyu Zou, Wen Xia, Sian Jin, Dingwen Tao, Yang Liu, Weizhe Zhang, and Zheng Zhang.

Delta-DNN: Efficiently Compressing Deep Neural Networks via Exploiting Floats Similarity.

Proceedings of the 49th International Conference on Parallel Processing, Edmonton, AB, Canada, August 17-20 2020. Acceptance Rate: 28% (78/269). [ Paper ] [ Talk ]

HPDC'20

top-cs, top-cr

Kai Zhao, Sheng Di, Xin Liang, Sihuan Li, Dingwen Tao, Zizhong Chen, and Franck Cappello.

Significantly Improving Lossy Compression for HPC Datasets with Second-Order Prediction and Parameter Optimization.

Proceedings of the 29th ACM International Symposium on High-Performance Parallel and Distributed Computing, Stockholm, Sweden, June 23-26, 2020. Acceptance Rate: 22% (16/71). [ Paper ] [ Talk ]

IPDPS'20

top-cr

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 Processing Symposium, New Orleans, LA, May 18-22, 2020. Acceptance Rate: 24% (110/446) [ Paper ] [ Slides ] [ Code ]

DAC'20

top-cs, top-cr

Peiyan Dong, Siyue Wang, Wei Niu, Chengming Zhang, Sheng Lin, Zhengang Li, Yifan Gong, Bin Ren, Xue Lin, and Dingwen Tao*.

RTMobile: Beyond Real-Time Mobile Acceleration of RNNs for Speech Recognition.

Proceedings of the 57th Annual Design Automation Conference, San Francisco, CA, USA, July 19-23, 2020. Acceptance Rate: 23% (228/984). [ Paper ] [ Talk ]

TPDS'20

top journal, impact factor: 4.181

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

Performance Optimization for Relative-Error-Bounded Lossy Compression on Scientific Data.

IEEE Transactions on Parallel and Distributed Systems 31, no. 7 (2020): 1665-1680. [ Paper ]

PPOPP'20

top-cr

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. Acceptance Rate: 23% (28/121)  [ Paper ] [ Slides ]

TPDS'19

top journal, impact factor: 4.181

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 30, no. 8 (2019): 1857-1871. [ Paper ]

HPDC'19

top-cs, top-cr

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% (22/106) [ Paper ] [ Slides ] [ Code ]

SC'19

top-cs, top-cr

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% (78/344) [ Paper ]

ICS'19

top-cs, top-cr

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% (45/193) [ Paper ] [ Slides ] [ Code ]

CLUSTER'19

top-cr

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% (39/141) [ Paper ]

TPDS'18

top journal, impact factor: 4.181

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 30, no. 2 (2018): 331-345. [ Paper ]

CLUSTER'18

top-cr

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: 0.6% (1/164) [ Paper ] [ Slides ] [ Code

Best Paper Award

CLUSTER'18

top-cr

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% (4/154) [ Paper ] [ Slides ] [ Code ]

Best Paper Award in Data, Storage, Visualization Area

CLUSTER'18

top-cr

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% (49/154) [ Paper ] [ Slides ]

HPDC'18

top-cs, top-cr

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% (22/112) [ Paper ][ Slides ] [ Code

IPDPS'17

top-cr

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 and Distributed Processing Symposium, Orlando, Florida, USA, May 29-June 2, 2017. Acceptance Rate: 22% (116/508). [ Paper ] [ Slides ] [ Code ]

HPDC'16

top-cs, top-cr

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% (20/129). [ Paper ] [ Slides ]

 

SERVICES

ORGANIZATION COMMITTEE

  • Program Co-chair of 2021 IEEE International Conference on Scalable Computing & Communications (ScalCom)

  • Program Co-chair of the 1st and 2nd International Workshop on Big Data Reduction (IWBDR) co-held with IEEE BigData 2020 and 2021

  • Program Co-chair of the 1st and 2nd International Workshop on Big Data Analytics of Cyber-Physical Systems (CPSBigData) co-held with IEEE IGSC 2019 and 2020

TECHNICAL PROGRAM COMMITTEE

  • IEEE/ACM SC 2023, IEEE IPDPS 2023, ACM HPDC 2023

  • IEEE/ACM SC 2022, ACM HPDC 2022, IEEE/ACM CCGrid 2022,IEEE HiPC, IEEE BigData 2022, IEEE ICMLA 2022, IEEE SCC 2022

  • IEEE ICMLA 2021, IEEE HiPC 2021, IEEE IPDPS 2021, IEEE Cluster 2021, IEEE BigData 2021

  • IEEE/ACM SC 2020, IEEE Cluster 2020, IEEE Big Data 2020, ICPP 2020, IFIP NPC 2020, IEEE SCC 2020

  • IEEE HiPC 2019, IFIP NPC 2019, IEEE BigData Congress 2019, IEEE SCC 2019

  • IEEE HiPC 2018, IFIP NPC 2018, IEEE BigData Congress 2018, IEEE eScience 2018

JOURNAL REVIEW

IEEE Transactions on Computers / Parallel and Distributed Systems / Cloud Computing / Big Data / Sustainable Computing / Smart Grid / Emerging Topics in Computing, IEEE Access, SIAM Journal on Scientific Computing, Scientific Programming, Parallel Computing, Journal of Supercomputing, Journal of Systems Architecture, Integration the VLSI Journal

GRANT REVIEW

  • Panelist for U.S. National Science Foundation (NSF) in 2022

  • Proposal Reviewer for National Research Foundation of Ukraine (NRFU) in 2021

  • Panelist for U.S. National Science Foundation (NSF) in 2021

  • Proposal Reviewer for U.S. National Aeronautics and Space Administration (NASA) in 2020

 

WELCOME TO VISIT!

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