NSFCAREER: Data Polymorphism: Enabling Fast and Adaptable Scientific Data Retrieval with Progressive Representations

Project description

CAREER: Data Polymorphism: Enabling Fast and Adaptable Scientific Data Retrieval with Progressive Representations

Scientific simulations and instruments are producing an unprecedented amount of data that overwhelms the network and storage systems. These data have to be stored at remote sites or moved to secondary storage for archival purposes due to the limited capacity in high end parallel file systems. This poses grand challenges to fetching the data for post hoc data analytics, as the data movement bandwidth across wide area networks or from secondary systems is very limited. This project bridges this gap by developing scalable software to realize data polymorphism, a novel paradigm that allows for variable representations of the same data under different scenarios and use cases, to enable on demand data provision with reduced data movement cost. The success of this project is expected to significantly reduce the time needed to gain scientific insights from data for a wide range of applications, thus advancing scientific discoveries in domains including climatology, cosmology, fusion energy science, and ptychography. This contributes to resolving a wide range of important societal problems, including weather forecasting, galaxy surveys, electric generation, and material design. Furthermore, an integrated education program is developed for workforce development and broadening participation in advanced cyberinfrastructure.

Publications

HPDC'26

Wenbo Li, Qian Gong, Xuan Wu, Jieyang Chen, Qing Liu, Xubin He, Norbert Podhorszki, Scott Klasky, and Xin Liang.
QProR: An Efficient Framework for Quantity-of-Interest Based Progressive Retrieval with Guaranteed Error Control.
Proceedings of the 35th ACM International Symposium on High-Performance Parallel and Distributed Computing, Cleveland, OH, July 13 - 16, 2026. [DOI]

ICDE'26

Mingze Xia, Yuxiao Li, Pu Jiao, Bei Wang, Xin Liang*, and Hanqi Guo.
Time-varying Vector Field Compression with Preserved Critical Point Trajectories.
Proceedings of the 42nd IEEE International Conference on Data Engineering, Montreal, Canada, May 4 - 8, 2026. (*: Corresponding author) [DOI]

ICDE'26

Xuan Wu, Sheng Di, Tripti Agarwal, Kai Zhao, Xin Liang*, and Franck Cappello.
Enabling Homomorphic Analytical Operations on Compressed Scientific Data with Multi-stage Decompression.
Proceedings of the 42nd IEEE International Conference on Data Engineering, Montreal, Canada, May 4 - 8, 2026. (*: Corresponding author) [DOI]

SC'25

Yanliang Li+, Wenbo Li+, Qian Gong, Qing Liu, Norbert Podhorszki, Scott Klasky, Xin Liang, and Jieyang Chen.
HP-MDR: High-performance and Portable Data Refactoring and Progressive Retrieval with Advanced GPUs.
Proceedings of the 2025 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, St. Louis, MO, USA, Nov 16 - 21, 2025. Acceptance Rate: 22.0% (137/623). (+: Co-first authors) [DOI]