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UID:submissions.supercomputing.org_SC25_sess280_pap556@linklings.com
SUMMARY:HP-MDR: High-Performance and Portable Data Refactoring and Progres
 sive Retrieval with Advanced GPUs
DESCRIPTION:Yanliang Li (University of Oregon), Wenbo Li (University of Ke
 ntucky), Qian Gong (Oak Ridge National Laboratory (ORNL)), Qing Liu (New J
 ersey Institute of Technology (CSLA)), Norbert Podhorszki and Scott Klasky
  (Oak Ridge National Laboratory (ORNL)), Xin Liang (University of Kentucky
 ), and Jieyang Chen (University of Oregon)\n\nScientific applications prod
 uce vast amounts of data, posing grand challenges for data management and 
 analytics. Progressive compression is an approach to address this problem,
  as it allows for on-demand data retrieval with significantly reduced data
  movement cost. This work proposes HP-MDR, a high-performance and portable
  data refactoring and progressive retrieval framework for GPUs. Our contri
 butions are threefold: (1) we optimize the bit-plane encoding and lossless
  encoding to achieve high performance on GPUs; (2) we propose pipeline opt
 imization to further enhance the performance for large data processing; (3
 ) we leverage our framework to enable data retrieval with guaranteed error
  control for quantities-of-interest; (4) we evaluate HP-MDR using five dat
 asets. Evaluations demonstrate HP-MDR achieves 13.68x and 6.31x average th
 roughput improvement for refactoring and progressive retrieval, respective
 ly. It also leads to 11.22x throughput for recomposing data under quantity
 -of-interest error control and 6.04x performance for the corresponding end
 -to-end data retrieval compared with state-of-the-art solutions.\n\nTag: D
 ata Analytics, Visualization & Storage\n\nRecording: Livestreamed, Recorde
 d\n\nRegistration Category: Technical Program Reg Pass\n\nSession Chair: J
 anine C. Bennett (Sandia National Laboratories)\n\n
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