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UID:submissions.supercomputing.org_SC23_sess431@linklings.com
SUMMARY:The 9th International Workshop on Data Analysis and Reduction for 
 Big Scientific Data (DRBSD-9)
DESCRIPTION:Analyzing Impact of Data Reduction Techniques on Visualization
  for AMR Applications Using AMReX Framework\n\nToday’s scientific simulati
 ons generate exceptionally large volumes of data, challenging the capaciti
 es of available I/O bandwidth and storage space. This necessitates a subst
 antial reduction in data volume, for which error-bounded lossy compression
  has emerged as a highly effective strategy. ...\n\n\nDaoce Wang (Indiana 
 University), Jesus Pulido and Pascal Grosset (Los Alamos National Laborato
 ry (LANL)), Jiannan Tian (Indiana University), James Ahrens (Los Alamos Na
 tional Laboratory (LANL)), and Dingwen Tao (Indiana University)\n---------
 ------------\nOpening Remarks and Welcome\n\nDingwen Tao (Indiana Universi
 ty) and Sheng Di (Argonne National Laboratory (ANL))\n--------------------
 -\nClosing Remarks\n\nSheng Di (Argonne National Laboratory (ANL))\n------
 ---------------\nLibPressio-Predict: Flexible and Fast Infrastructure For 
 Inferring Compression Performance\n\nOver recent years, substantial effort
 s have gone into developing systems to infer compression performance witho
 ut running compressors. These efforts have driven down the error in the es
 timates, reduced their runtimes, and improved their generality. However, t
 hese efforts are uncoordinated increasing...\n\n\nRobert R. Underwood and 
 Sheng Di (Argonne National Laboratory (ANL), University of Chicago); Sian 
 Jin (Indiana University); Md Hasanur Rahman (University of Iowa); Arham Kh
 an (University of Chicago); and Franck Cappello (Argonne National Laborato
 ry (ANL), University of Chicago)\n---------------------\nThe 9th Internati
 onal Workshop on Data Analysis and Reduction for Big Scientific Data (DRBS
 D-9)\n\nIn this new exascale computing era, applications must increasingly
  perform online data analysis and reduction—tasks that introduce algorithm
 ic, implementation, and programming model challenges that are unfamiliar t
 o many scientists and that have major implications for the design and use 
 of var...\n\n\nSheng Di (Argonne National Laboratory (ANL)), Jieyang Chen 
 (University of Alabama), Shadi Ibrahim (INRIA), Xin Liang (University of K
 entucky), and Ana Gainaru (Oak Ridge National Laboratory (ORNL))\n--------
 -------------\nFast 2D Bicephalous Convolutional Autoencoder for Compressi
 ng 3D Time Projection Chamber Data\n\nHigh-energy large-scale particle col
 liders produce data at high speed in the order of 1 terabytes per second i
 n nuclear physics and 1 petabytes per second in high-energy physics. Time 
 projection chamber tracking detector data are usually very sparse, which p
 resents a challenge to conventional learni...\n\n\nYi Huang, Yihui Ren, Sh
 injae Yoo, and Jin Huang (Brookhaven National Laboratory)\n---------------
 ------\nWhat Operations Can Be Performed Directly on Compressed Arrays and
  with What Error?\n\nIn response to the rapidly escalating data movement-r
 elated costs of computing with large matrices and tensors, several lossy c
 ompression methods have been developed that help reduce the volume of data
  moved. Unfortunately, all these methods require the data to be decompress
 ed before operating on th...\n\n\nTripti Agarwal, Harvey Dam, and Ganesh G
 opalakrishnan (University of Utah) and Dorra Ben Khalifa and Matthieu Mart
 el (University of Perpignan, France)\n---------------------\nLossy and Los
 sless Compression for BioFilm Optical Coherence Tomography (OCT)\n\nOptica
 l Coherence Tomography (OCT) is a fast and nondestructive technology for b
 acterial biofilm imaging. However, OCT generates approximately 100 GB per 
 flow cell, complicating storage and data sharing. Data reduction reduces d
 ata complications by reducing the overhead and amount of data transferre..
 .\n\n\nMax Faykus III, Jon Calhoun, and Melissa Smith (Clemson University)
 \n---------------------\nStreaming Hardware Compressor Generator Framework
 \n\nThe interest in and strong demand for application-specific accelerator
 s in computing and sensor data processing are rising. Simultaneously, data
  movement bottlenecks are increasingly becoming a significant limiting fac
 tor for these accelerators. Integrating an extremely resource-efficient, u
 ltra-low...\n\n\nKazutomo Yoshii (Argonne National Laboratory (ANL)), Tomo
 hiro Ueno and Kentaro Sano (RIKEN Center for Computational Science (R-CCS)
 ), and Antonino Miceli and Franck Cappello (Argonne National Laboratory (A
 NL))\n---------------------\nScientific Data Democratization: Enabling Eff
 icient Access and Analysis of Large-Scale Scientific Data\n\nThe vast amou
 nts of data generated by scientific research hold immense potential for ad
 vancing knowledge and discovery. However, the complexity and sheer volume 
 of this data often pose significant challenges in terms of accessibility, 
 analysis, and interpretation. Scientific data democratization aim...\n\n\n
 Scott Klasky (Oak Ridge National Laboratory (ORNL))\n\nTag: Data Analysis,
  Visualization, and Storage, Data Compression\n\nRegistration Category: Wo
 rkshop Reg Pass\n\nSession Chairs: Sheng Di (Argonne National Laboratory (
 ANL), University of Chicago); Dingwen Tao (Institute of Computing Technolo
 gy, Chinese Academy of Sciences; University of Chinese Academy of Sciences
 ); Ana Gainaru (Oak Ridge National Laboratory (ORNL)); Jieyang Chen (Unive
 rsity of Oregon); Shadi Ibrahim (French Institute for Research in Computer
  Science and Automation (INRIA)); and Xin Liang (Oregon State University)
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