Jianɴan Tian

short bio

Jiannan Tian received his Ph.D. degree in Intelligent Systems Engineering (Computer Engineering Track). His research interests include heterogeneous high-performance computing,  big-data analytics, AI/ML-aided large-scale data processing and accelerated scientific compression. During his Ph.D. study, he works as a student intern at Argonne National Laboratory (ANL). He is also the leading developer of open-source pSZ/cuSZ (link), a GPU-accelerated error-bounded lossy compressor for scientific data.

selected publication

[SC '24] Jinyang Liu *, Jiannan Tian *, Shixun Wu *, Sheng Di, Boyuan Zhang, Yafan Huang, Kai Zhao, Guanpeng Li, Dingwen Tao, Zizhong Chen, and Franck Cappello. "cuSZ-I: High-Fidelity Error-Bounded Lossy Compression for Scientific Data on GPUs." Supercomputing Conference 2024, Atlanta, GA, November 12–17. (* Equal contribution.)

[VLDB '24] Xinyu Chen, Jiannan Tian, Ian Beaver, Cynthia Freeman, Jianguo Wang, and Dingwen Tao. “FCBench: Cross-Domain Benchmarking of Lossless Compression for Floating-point Data [Experiment, Analysis & Benchmark].” 50th International Conference on Very Large Databases, Guangzhou, China (and hybrid), August 25–29, 2024. 

[TBD '23] Xin Liang, Kai Zhao, Sheng Di, Sihuan Li, Robert Underwood, Ali M. Gok, Jiannan Tian, Junjing Deng, Jon C. Calhoun, Dingwen Tao, Zizhong Chen, and Franck Cappello. “SZ3: A Modular Framework for Composing Prediction-Based Error-Bounded Lossy Compressors.” IEEE Transactions on Big Data.
* This paper has been awarded the 2023 Best Paper Award by the IEEE Computer Society Publications Board.

[HPDC '23] Boyuan Zhang *, Jiannan Tian *, Sheng Di, Xiaodong Yu, Dingwen Tao, and Franck Cappello. “Fast GPU Lossy Compressor for Scientific Computing Applications.” The ACM International Symposium on High-Performance Parallel and Distributed Computing (in conjunction with the Federated Computing Research Conference), Orlando, Florida, June 20–23, 2023. (* Equal contribution.)

[CLUSTER '21] 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, (Virtual Event) Portland, OR, September 7–10, 2021.

[IPDPS '21] Jiannan Tian, Cody Rivera, Jieyang Chen, Dingwen Tao, Sheng Di, and Franck Cappello. “Revisiting Huffman Coding: Toward Extreme Performance on Modern GPU Architectures.” IEEE International Parallel & Distributed Processing Symposium, (Virtual Event) Portland, OR, May 17–21, 2021.

[PACT '20] Jiannan Tian, Sheng Di, Kai Zhao, Cody Rivera, Megan Hickman Fulp, Robert Underwood, Sian Jin, Xin Liang, Jon Calhoun, Dingwen Tao, and Franck Cappello. “cuSZ: A High-Performance GPU Based Lossy Compression Framework for Scientific Data.” The 29th International Conference on Parallel Architectures and Compilation Techniques, (Virtual Event) Atlanta, GA, October 3–7, 2020.

[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, CA, February 22–26, 2020.

full publication list

Please refer to the full list on Google Scholar. Please also refer to my CV (PDF).