jiannan tian

Jiannan Tian is currently Ph.D. Student at Indiana University ( jti1 'at' iu.edu ). His research interests include High-Performance Computing, Big-Data Analytics, heterogeneous computing, and Lossy Compression for Scientific Data. During his ongoing Ph.D. study, he works as a student intern at Argonne National Laboratory (ANL). He is also the main developer of open-source cuSZ, a GPU-accelerated error-bounded lossy compressor for scientific data.

publication

  • [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 Huff- man 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.

publication (cont'ed)

  • [IPDPS ’23] (in submission) Boyuan Zhang, Jiannan Tian, Sheng Di, Xiaodong Yu, Dingwen Tao, and Franck Cappello.

  • [PPoPP ’23] (to resubmit) Boyuan Zhang, Jiannan Tian, Sheng Di, Xiaodong Yu, Lizhi Xiang, Dingwen Tao, and Franck Cappello.

  • [MASCOTS ’22] Griffin Dube, Jiannan Tian, Sheng Di, Dingwen Tao, Jon C. Calhoun, and Franck Cappello. “Efficient Error-Bounded Lossy Compression for CPU Architectures.” 30th International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, Nice, France, October 18–20, 2022.

publication (coauthored)

See the full list.