GWLZ: A Group-wise Learning-based Lossy Compression Framework for Scientific Data

The rapid expansion of computational capabilities and the evergrowing The rapid growth of computational power has facilitated the execution scale of modern HPC systems present formidable challenges of complex scientific simulations across various fields of in managing exascale scientific data. Faced with such vast science. Users harness supercomputers to conduct these simulations datasets, traditional lossless compression techniques prove insufficient and extract insights from the resulting data. However, despite in reducing data size to a manageable level while preserving the acceleration of simulations provided by supercomputers, users all information intact. In response, researchers have turned to errorbounded often encounter limitations in data storage and internet bandwidth lossy compression methods, which offer a balance between on their end, as they may need to analyze the data locally, and some data size reduction and information retention. However, despite users also need to distribute large volumes of data across multiple their utility, these compressors employing conventional techniques endpoints via a data-sharing web service.