©2019 by Dingwen Tao

FULL PUBLICATION

Refereed Conference Publications

  1. 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 (SC'19), Denver, Colorado, USA, Nov 17 - 22, 2019.

  2. Xiangyu Zou, Tao Lu, Sheng Di, Dingwen Tao, Wen Xia, Xuan Wang, Weizhe Zhang, and Qing Liao. “Accelerating Lossy Compression on HPC datasets via Partitioning Computation for Parallel Processing”. Proceedings of the 21st IEEE International Conference on High Performance Computing and Communications (HPCC'19), Zhangjiajie, China, Aug 10 - 12, 2019.

  3. 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 (ICS'19), Phoenix, AZ, USA, June 26 - 28, 2019.

  4. 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 (HPDC'19), Phoenix, AZ, USA, June 24 - 28, 2019.

  5. 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 (MSST'19), Santa Clara, CA, USA, May 20 - 24, 2019.

  6. 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 (BigData'18), Seattle, WA, December 10 – 13, 2018.

  7. Line Pouchard, Kevin Huck, Gyorgy Matyasfalvi, Dingwen Tao, Li Tang, Huub Van Dam, Shinjae Yoo. “Prescriptive provenance for streaming analysis of workflows at scale”. Proceedings of 2018 New York Scientific Data Summit (NYSDS'18), New York, NY, USA, November 16, 2018.

  8. Dingwen Tao, Sheng Di, Xin Liang, Zizhong Chen, and Franck Cappello. “Design of Fixed-PSNR Lossy Compression for HPC Scientific Data”. Proceedings of the 2018 IEEE International Conference on Cluster Computing (CLUSTER'18), Belfast, UK, September 10 – June 13, 2018.

  9. 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 (CLUSTER'18), Belfast, UK, September 10 – June 13, 2018.

  10. Ali Murat Gok, Sheng Di, Yury Alexeev, Dingwen Tao, Vladimir Mironov, 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 (CLUSTER'18), Belfast, UK, September 10 – June 13, 2018.

  11. Jieyang Chen, Hongbo Li, Sihuan Li, Xin Liang, Panruo Wu, Dingwen Tao, Kaiming Ouyang, Yuanlai Liu, Kai Zhao, 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 (SC'18), Dallas, Texas, USA, Nov 11 – 16, 2018.

  12. 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 (HPDC'18), Tempe, AZ, USA, June 11 – June 15, 2018.

  13. Dingwen Tao, Sheng Di, Zizhong Chen, and Franck Cappello. “In-Depth Exploration of Single-Snapshot Lossy Compression Techniques for N-Body Simulations”. Proceedings of 2017 IEEE International Conference on Big Data (BigData'17), Boston, MA, USA, December 11 – 14, 2017.

  14. 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 31th IEEE International Parallel & Distributed Processing Symposium (IPDPS'17), Orlando, Florida, USA, May 29 – June 2, 2017.

  15. 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 (SC'17), Denver, Colorado, USA, Nov 12 – 17, 2017.

  16. Ian Foster, Mark Ainsworth, Bryce Allen, Julie Bessac, Franck Cappello, Jong Youl Choi, Emil Constantinescu, Philip E Davis, Sheng Di, Wendy Di, Hanqi Guo, Scott Klasky, Kerstin Kleese Van Dam, Tahsin Kurc, Qing Liu, Abid Malik, Kshitij Mehta, Klaus Mueller, Todd Munson, George Ostouchov, Manish Parashar, Tom Peterka, Line Pouchard, Dingwen Tao, Ozan Tugluk, Stefan Wild, Matthew Wolf, Justin M Wozniak, Wei Xu, Shinjae Yoo. “Computing Just What You Need: Online Data Analysis and Reduction at Extreme Scales”. Proceedings of the 23rd International European Conference on Parallel and Distributed Computing (EuroPar'17), Santiago de Compostela, Spain, Aug 28 – Sept 1, 2017.

  17. Panruo Wu, Nathan Debardeleben, Qiang Guan, Sean Blanchard, Jieyang Chen, Dingwen Tao, Xin Liang, Kaiming Ouyang, 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 (PPoPP'17), Austin, Texas, USA, February 4 – 8, 2017.

  18. 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 (SC'16), Salt Lake City, Utah, USA.

  19. Dingwen Tao, Shuaiwen Leon Song, Sriram Krishnamoorthy, Panruo Wu, Xin Liang, Eddy Z. 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 (HPDC'16), Kyoto, JAPAN, May 31– June 4, 2016.

  20. 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 (HPDC'16), Kyoto, JAPAN, May 31 – June 4, 2016.

  21. Longxiang Chen, Dingwen Tao, Panruo, and Zizhong Chen. “Extending Checksum-Based ABFT to Tolerate Soft Errors Online in Iterative Methods”. Proceedings of the 20th IEEE International Conference on Parallel And Distributed Systems (ICPADS'14), Hsinchu, Taiwan, December 16 – 19, 2014.

 

Refereed Journal Articles

  1. Franck Cappello, Sheng Di, Sihuan Li, Xin Liang, Ali Murat Gok, Dingwen Tao, Chun Hong Yoon, Xin-Chuan Wu, Yuri Alexeev, and Frederic T. Chong. “Use Cases of Lossy Compression for Floating-point Data in Scientific Datasets”. The International Journal of High Performance Computing Applications (2019).

  2. 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 (2019).

  3. 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 (2018).

  4. 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 (2017).

 

Workshop Publications and Posters

  1. Dingwen Tao. “DeepSZ: A Novel Framework to Compress DNNs by Using Error-Bounded Lossy Compression”. Poster in the 3rd ACSIC Symposium on Frontiers in Computing (SOFC), Chicago, Illinois, USA, June 7 – 8, 2019.

  2. Dingwen Tao. “Exascale Lossy Compression for Scientific Data”. Poster in the 2nd ACSIC Symposium on Frontiers in Computing (SOFC), Dallas, Texas, USA, June 1 – 2, 2018.

  3. Sheng Di, Dingwen Tao, and Franck Cappello. “An Efficient Approach to Lossy Compression with Pointwise Relative Error Bound”. Proceedings of the 2nd International Workshop on Data Reduction for Big Scientific Data in Conjunction with SC’17 (DRBSD-2), Denver, Colorado, USA, Nov 12 – 17, 2017.

  4. Ali Murat Gok, Sheng Di, Yury Alexeev, Dingwen Tao, Vladimir Mironov, and Franck Cappello. "A Novel Data Compression Algorithm for Two-Electron Repulsion Energy Integrals". Poster in the 29th ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (SC'17), Denver, Colorada, 2017.

  5. Dingwen Tao, Sheng Di, Zizhong Chen, and Franck Cappello. “Exploration of Pattern-Matching Techniques for Lossy Compression on Cosmology Simulation Data Sets”. Proceedings of the 1st International Workshop on Data Reduction for Big Scientific Data in Conjunction with ISC'17 (DRBSD-1), Frankfurt, Germany, June 22, 2017.

  6. Dingwen Tao, Sheng Di, Zizhong Chen, and Franck Cappello. “Towards Efficient Error-Controlled Lossy Compression for Scientific Data”. Poster in the 6th Greater Chicago Area Systems Research Workshop (GCAR), Illinois Institute of Technology, McCormick Tribune Campus Center, April 24, 2017.

 

Thesis Publication

Dingwen Tao, "Fault Tolerance Techniques for High-Performance Iteartive Methods", University of California, Riverside, 2018.