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UID:submissions.supercomputing.org_SC23_sess303_rpost124@linklings.com
SUMMARY:Integrating TEZIP into LibPressio:  A Case Study of Integrating a 
 Dynamic Application into a Static C Environment
DESCRIPTION:Isita Talukdar (University of California, Berkeley; RIKEN Cent
 er for Computational Science (R-CCS)); Amarjit Singh (RIKEN Center for Com
 putational Science (R-CCS)); Robert Underwood (Argonne National Laboratory
  (ANL)); Kento Sato (RIKEN Center for Computational Science (R-CCS)); and 
 Weikuan Yu (Florida State University)\n\nLCLS-II at SLAC, SNS at Oak Ridge
  Laboratory, and other instruments use software written in C and C++, prod
 ucing huge volumes of time evolving data at high rate. Data compression ca
 n decrease the volume of data we need to move and store. TEZIP is a neural
  network (NN) based compressor designed for high-quality compression of ti
 me-evolving data. However, TEZIP is written in Python and is not easily us
 able from or ported to C++. In this work, we develop new components in Lib
 Pressio that allow us to integrate with TEZIP and other external compresso
 rs efficiently and evaluate them with a systematic approach. We find that 
 TEZIP’s compression ratio (Error Bound 1e-06) for Hurricane Isabel is 128,
  which is 2.4 times greater than the leading SZ3’s, 52.8. Our basic integr
 ation of TEZIP into Libpressio sets a precedent for the integration of non
  C/C++ compressors into LibPressio.\n\nTag: Artificial Intelligence/Machin
 e Learning, Architecture and Networks, Heterogeneous Computing, I/O and Fi
 le Systems, Performance Measurement, Modeling, and Tools, Post-Moore Compu
 ting, Programming Frameworks and System Software, Quantum Computing\n\nReg
 istration Category: Tech Program Reg Pass, Exhibits Reg Pass\n\n
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