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DTSTAMP:20260422T143138Z
LOCATION:B304
DTSTART;TZID=America/New_York:20241118T115500
DTEND;TZID=America/New_York:20241118T121000
UID:submissions.supercomputing.org_SC24_sess749_ws_drbsd113@linklings.com
SUMMARY:GPUFastqLZ: An Ultra Fast Compression Methodology for Fastq Sequen
 ce Data on GPUs
DESCRIPTION:Taolue Yang, Youyuan Liu, Bo Jiang, and Sian Jin (Temple Unive
 rsity)\n\nWe present gpuFastqLZ, an ultra-fast compression methodology for
  FASTQ sequence data on GPUs. Leveraging the high parallelism capabilities
  of GPUs, gpuFastqLZ incorporates several optimizations, including a fast 
 algorithm for field separation, a 2-bit encoding scheme for base fields, a
 nd the implementation of Illumina binning and GPULZ compression algorithms
 . \nWe evaluate gpuFastqLZ on three datasets, across 324 hyperparameter se
 ttings, which shows that gpuFastqLZ outperforms existing compressors, achi
 eving up to a 1300x speedup in compression throughput and a 1.1x improveme
 nt in compression ratio compared to GZIP and exceeds the state-of-the-art 
 FASTQ compressor GENOZIP by up to 18X throughput.\n\nTag: Data Compression
 , Data Movement and Memory, Middleware and System Software\n\nRegistration
  Category: Workshop Reg Pass\n\nSession Chairs: Sheng Di (Argonne National
  Laboratory (ANL), University of Chicago); Ana Gainaru (Oak Ridge National
  Laboratory (ORNL)); Sian Jin (Temple University); Xin Liang (Oregon State
  University); Kento Sato (RIKEN Center for Computational Science (R-CCS));
  and Dingwen Tao (Institute of Computing Technology, Chinese Academy of Sc
 iences; University of Chinese Academy of Sciences)\n\n
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