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DTSTART;TZID=America/New_York:20241119T113000
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UID:submissions.supercomputing.org_SC24_sess377_pap404@linklings.com
SUMMARY:cuSZp2: A GPU Lossy Compressor with Extreme Throughput and Optimiz
 ed Compression Ratio
DESCRIPTION:Yafan Huang (University of Iowa), Sheng Di (Argonne National L
 aboratory (ANL)), Guanpeng Li (University of Iowa), and Franck Cappello (A
 rgonne National Laboratory (ANL))\n\nExisting GPU lossy compressors suffer
  from expensive data movement overheads, inefficient memory access pattern
 s, and high synchronization latency, resulting in limited throughput. This
  work proposes cuSZp2, a generic single-kernel error-bounded lossy compres
 sor purely on GPUs designed for applications that require high speed, such
  as large-scale GPU simulation and large language model training. In parti
 cular, cuSZp2 proposes a novel lossless encoding method, optimizes memory 
 access patterns, and hides synchronization latency, achieving extreme end-
 to-end throughput and optimized compression ratio. Experiments on NVIDIA A
 100 GPU with nine real-world HPC datasets demonstrate that, even with high
 er compression ratios and data quality, cuSZp2 can deliver on average 332.
 42 and 513.04 GB/s end-to-end throughput for compression and decompression
 , respectively, which is around 2× of existing pure-GPU compressors and 20
 0× of CPU-GPU hybrid compressors.\n\nTag: Accelerators, Algorithms, Data C
 ompression, I/O, Storage, Archive, Performance Optimization\n\nRegistratio
 n Category: Tech Program Reg Pass\n\nAward Finalist: Best Student Paper Fi
 nalist\n\nSession Chair: Ganesh Gopalakrishnan (University of Utah)\n\n
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