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DTSTART;TZID=America/New_York:20241118T121000
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UID:submissions.supercomputing.org_SC24_sess749_ws_drbsd114@linklings.com
SUMMARY:Accelerating Viz Pipelines Using Near-Data Computing: An Early Exp
 erience
DESCRIPTION:Qing Zheng (Los Alamos National Laboratory (LANL), New Mexico 
 Consortium); Brian Atkinson (Los Alamos National Laboratory (LANL)); Daoce
  Wang (Indiana University); and Jason Lee, John Patchett, Dominic Manno, a
 nd Gary Grider (Los Alamos National Laboratory (LANL))\n\nTraditional scie
 ntific visualization pipelines transfer entire data arrays from storage to
  client nodes for processing into displayable graphics objects. However, t
 his full data transfer is often unnecessary, as many visualization filters
  operate on only small subsets of data in a data array. With the rise of c
 omputational storage, smart NICs, and smart devices enabling offloaded pro
 cessing, this paper examines a case where a visualization pipeline is divi
 ded into pre-filters that run near data and post-filters that execute on t
 he client side. Pre-filters preprocess the data near it on storage nodes, 
 reducing data volumes before transfer based on downstream pipeline needs, 
 while post-filters complete the processing on the client node. Experiments
  done on two real-world simulation datasets demonstrate that this approach
  can significantly reduce network transfer volumes, cutting visualization 
 pipeline data load times by up to 2.8X compared to traditional methods, an
 d up to 11.9X when combined with data compression techniques.\n\nTag: Data
  Compression, Data Movement and Memory, Middleware and System Software\n\n
 Registration Category: Workshop Reg Pass\n\nSession Chairs: Sheng Di (Argo
 nne National Laboratory (ANL), University of Chicago); Ana Gainaru (Oak Ri
 dge National Laboratory (ORNL)); Sian Jin (Temple University); Xin Liang (
 University of Kentucky); Kento Sato (RIKEN); and Dingwen Tao (Institute of
  Computing Technology, Chinese Academy of Sciences; University of Chinese 
 Academy of Sciences)\n\n
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