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DTSTART;TZID=America/Denver:20231112T145000
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UID:submissions.supercomputing.org_SC23_sess431_ws_drbsd104@linklings.com
SUMMARY:LibPressio-Predict: Flexible and Fast Infrastructure For Inferring
  Compression Performance
DESCRIPTION:Robert R. Underwood and Sheng Di (Argonne National Laboratory 
 (ANL), University of Chicago); Sian Jin (Indiana University); Md Hasanur R
 ahman (University of Iowa); Arham Khan (University of Chicago); and Franck
  Cappello (Argonne National Laboratory (ANL), University of Chicago)\n\nOv
 er recent years, substantial efforts have gone into developing systems to 
 infer compression performance without running compressors. These efforts h
 ave driven down the error in the estimates, reduced their runtimes, and im
 proved their generality. However, these efforts are uncoordinated increasi
 ng the efforts required to perform comparisons between them. There may be 
 subtle differences in sampling approaches, and nuances to the interfaces r
 equiring efforts to port applications between them and to reproduce experi
 ments. Additionally, many of these methods call for substantial amounts of
  training data to produce reliable estimates, as well as scalable codes to
  perform the training. In this work, we present LibPressio-Predict -- a sc
 alable library for use in applications using predictions of compression pe
 rformance and a scalable tool LibPressio-Bench to run these experiments qu
 ickly at scale. We use this tool to evaluate 3 recent compression predicti
 on approaches systematically with all 48 timesteps and 13 fields Hurricane
  Issable dataset.\n\nTag: Data Analysis, Visualization, and Storage, Data 
 Compression\n\nRegistration Category: Workshop Reg Pass\n\nSession Chairs:
  Sheng Di (Argonne National Laboratory (ANL), University of Chicago); Ding
 wen Tao (Institute of Computing Technology, Chinese Academy of Sciences; U
 niversity of Chinese Academy of Sciences); Ana Gainaru (Oak Ridge National
  Laboratory (ORNL)); Jieyang Chen (University of Oregon); Shadi Ibrahim (F
 rench Institute for Research in Computer Science and Automation (INRIA)); 
 and Xin Liang (Oregon State University)\n\n
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