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UID:submissions.supercomputing.org_SC23_sess308_spostg122@linklings.com
SUMMARY:ROI Preservation in Streaming Lossy Compression
DESCRIPTION:Avinash Kethineedi (Clemson University)\n\nToday’s state-of-th
 e-art scientific high-performance computing (HPC) applications generate ex
 tensive data in diverse domains, placing a significant strain on data tran
 sfer and storage systems. Most compression algorithms are more computation
 ally complex, requiring more processing power and time to compress and dec
 ompress data. However, these algorithms tend to achieve higher compression
  ratios resulting in smaller compressed data sizes. Real-time streaming ap
 plications demand high data throughput. Therefore, striking a right balanc
 e between compression efficiency and computational complexity is essential
 . This poster explores two key aspects: interpolation method of 'sz3' algo
 rithm for data reconstruction and the application of 'szx' algorithm on a 
 'Region of Interest(ROI)' - where a lesser data distortion is needed. We p
 erform a through evaluation using NYX scientific dataset. Experiments show
  that compression ratio is improved by ~2x. Compression and decompression 
 rates are improved by ~5-7x when contiguous ROI is preserved and only cert
 ain recursive levels of sx3 are processed.\n\nRegistration Category: Tech 
 Program Reg Pass\n\nSession Chair: Ana Gainaru (Oak Ridge National Laborat
 ory (ORNL))\n\n
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