Collaborative Research: Elements: ROCCI: Integrated Cyberinfrastructure for In Situ Lossy Compression Optimization Based on Post Hoc Analysis Requirements

Today’s simulations and advanced instruments are producing vast volumes of data, presenting a major storage and I/O burden for scientists. Error-bounded lossy compressors, which can significantly reduce the data volume while controlling data distortion with a constant error bound, have been developed for years. However, a significant gap still remains in practice.

On the one hand, the impact of the compression errors on scientific research is not well understood, so how to set an appropriate error bound for lossy compression is very challenging. On the other hand, how to select the best fit compression technology and run it automatically in scientific application codes is non-trivial because of strengths and weaknesses of different compression techniques and diverse characteristics of applications and datasets.

This project aims to develop a Requirement-Oriented Compression Cyber-Infrastructure (ROCCI) for data-intensive domains such as astrophysics and materials science, which can select and run the best fit lossy compressor automatically at runtime, in terms of user's requirement on their post hoc analysis.

The overarching goal of this project is to offer a complete series of automatic functions and services allowing users to transparently run the best fit compressor at runtime during the scientific simulations or data acquisition. This project advances knowledge and understanding with three key thrusts: (1) it builds an efficient layer to interoperate with different lossy compressors and diverse post hoc analysis requirements on data fidelity by leveraging an existing compression adaptor library (LibPressio) and compression assessment library (Z-checker); (2) it develops an efficient engine to determine the best fit compressor with optimized settings based on user’s post-hoc analysis requirements; and (3) it develops a user-friendly infrastructure that integrates compression optimization and execution via the HDF5 dynamic filter mechanism.

This project particularly targets cosmology and materials science applications and their specific requirements of using lossy compressors in practice.

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.