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DTSTART:19700308T020000
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DTSTAMP:20260422T143139Z
LOCATION:B304
DTSTART;TZID=America/New_York:20241118T090500
DTEND;TZID=America/New_York:20241118T095000
UID:submissions.supercomputing.org_SC24_sess749_misc301@linklings.com
SUMMARY:Invited Talk: Learning from Automatically Synthesized Compression 
 Algorithms
DESCRIPTION:Martin Burtscher (Texas State University)\n\nWe are generating
  data in larger amounts and at higher speeds than ever before. Data compre
 ssion is able to mitigate the resulting storage and transmission problems,
  but only if the compression ratio is high enough to obtain a meaningful b
 enefit and the throughput is sufficient to not introduce a new bottleneck.
  Machine learning can help by automatically synthesizing effective compres
 sion algorithms. In our work, we go a step further by employing such synth
 esis tools to extract valuable insights, which have enabled us to iterativ
 ely create more and more powerful compression algorithms. Ultimately, this
  has resulted in GPU-based compressors for scientific data that outperform
  the state of the art in throughput and compression ratio, both for lossle
 ss compression and for lossy compression with guaranteed point-wise error 
 bounds.\n\nTag: Data Compression, Data Movement and Memory, Middleware and
  System Software\n\nRegistration Category: Workshop Reg Pass\n\nSession Ch
 airs: Sheng Di (Argonne National Laboratory (ANL), University of Chicago);
  Ana Gainaru (Oak Ridge National Laboratory (ORNL)); Sian Jin (Temple Univ
 ersity); Xin Liang (Oregon State University); Kento Sato (RIKEN Center for
  Computational Science (R-CCS)); and Dingwen Tao (Institute of Computing T
 echnology, Chinese Academy of Sciences; University of Chinese Academy of S
 ciences)\n\n
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