OAC-TopoZ-Poster-2024

Today's large-scale simulations produce vast amounts of data that are revolutionizing scientific thinking and practices. While lossy compression is leveraged to address the big data challenges, most existing lossy compressors are agnostic of and thus fail to preserve topological features that are essential to scientific discoveries. This project aims to research and develop advanced lossy compression techniques and software that preserve topological features in data for in situ and post hoc analysis and visualization at extreme scales. In the first year, we developed two feature-preserving lossy compression methods: (1) MSZ, which preserves Morse segmentations in scalar fields; (2) sos-cpSZ, which enhances the critical point preservation in vector fields. Experimental results demonstrate that the proposed methods faithfully preserve the underlying topological features while reducing the data size, leading to improved data transfer and I/O performance in practice.