Collaborative Research: Frameworks: FZ: A fine-tunable cyberinfrastructure framework to streamline specialized lossy compression development

Data is the fourth pillar of the science methodology. However, rapidly expanding volumes and velocities of scientific data generated by simulation and instrument facilities present serious storage capacity, storage and network bandwidth, and data analysis challenges for many sciences. These challenges ultimately limit research discovery which would promote prosperity and welfare. Many research groups are exploring the use of data reduction techniques to address these challenges because lossy compression for scientific data offers a reliable, high-speed, and high-fidelity solution. However, existing generic lossy compressors often do not correspond to user-specific applications, use cases, and requirements in terms of reduction, speed, and information preservation. Hence, many potential users of lossy compressors for scientific data develop their own specialized lossy compression software, an effort that requires tremendous collaboration between compressor experts and domain scientists, demands extensive coding to optimize performance on multiple platforms, and often leads to redundant research and development efforts. This project aims to create a framework, called FZ, that revolutionizes the development of specialized lossy compressors by providing a comprehensive ecosystem to enable scientific users to intuitively research, compose, implement, and test specialized lossy compressors from a library of pre-developed, high-performance data reduction modules optimized for heterogeneous platforms. This project also contributes to the education and training of undergraduate and graduate students by enhancing the quality of computing-related curricula in scientific data management, compression, and visualization and through outreach activities at four universities. This project builds FZ, an intuitive cyberinfrastructure for the composition of specialized lossy compressors, by adapting, combining, and extending multiple existing capabilities from the SZ lossy compressor, the LibPressio unifying compression interface, the OptZConfig optimizer of compressor configurations, the Z-checker and QCAT compression quality analysis tools, and the Paraview and VTK visualization tools. The project has three thrusts: (1) It develops programming interfaces and a compressor generator to create new compressors from high-level languages such as Python and optimize their execution. (2) It refactors the SZ lossy compressors infrastructure to enable fine-grained composability of a large diversity of data transformation modules and integrate non-uniform compression capabilities, new preprocessing, decorrelation, approximation, and entropy coding data transformation modules to produce specialized lossy compressors. (3) It provides interactive visualization, quality assessment, and graphical user interface (GUI) tools that adapt and extend existing capabilities to automatically search optimized lossy compression module compositions and to identify relevant compression ratio, speed, and quality trade-offs for their use cases. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.