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UID:submissions.supercomputing.org_SC24_sess521_drs107@linklings.com
SUMMARY:Designing Efficient Data Reduction Approaches for Multi-Resolution
  Simulations on HPC Systems
DESCRIPTION:Daoce Wang (Indiana University)\n\nAs supercomputers advance t
 owards exascale capabilities, computational intensity increases significan
 tly, and the volume of data requiring storage and transmission experiences
  exponential growth. Multi-resolution methods, such as Adaptive Mesh Refin
 ement (AMR), have emerged as an effective solution to address these challe
 nges. Concurrently, error-bounded lossy compression is recognized as one o
 f the most efficient approaches to tackle the latter issue. Despite their 
 respective advantages, few attempts have been made to investigate how the 
 multi-resolution method and error-bounded lossy compression can function t
 ogether.\n\nTo address this gap, this dissertation introduces a series of 
 optimizations for data reduction solutions in multi-resolution simulations
 :\n\n(1) This dissertation first enhances the offline compression quality 
 of multi-resolution data for different state-of-the-art scientific compres
 sors by adaptively preprocessing the data and optimizing the compressor.\n
 \n(2) This dissertation then presents a novel in-situ lossy compression fr
 amework, utilizing HDF5 and enhanced SZ2, specifically tailored for real-w
 orld AMR applications. This framework can reduce I/O costs and improve com
 pression quality.\n\n(3) Furthermore, to extend the usability of multi-res
 olution techniques, this dissertation introduces a workflow for multi-reso
 lution data compression, applicable to both uniform and AMR simulations. I
 nitially, the workflow employs a Region of Interest (ROI) extraction appro
 ach to enable multi-resolution methods for uniform data. Subsequently, to 
 bridge the gap between multi-resolution techniques and lossy compressors, 
 we optimize three distinct compressors, ensuring their optimal performance
  on multi-resolution data. Lastly, we incorporate an advanced uncertainty 
 visualization method into our workflow to help users understand the potent
 ial impacts of lossy compression.\n\nRegistration Category: Tech Program R
 eg Pass\n\nSession Chair: Will Killian (NVIDIA Corporation)\n\n
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