Year: 2025
Type: preprint
Abstract: Particle-based simulations and point-cloud applications generate massive, irregular datasets that challenge storage, I/O, and real-time analytics. Traditional compression techniques struggle with irregular particle distributions and GPU architectural constraints, often resulting in limited throughput and suboptimal compression ratios. In this paper, we present GPZ, a high-performance, error-bounded lossy compressor designed specifically for large-scale particle data on modern GPUs. GPZ employs a novel four-stage parallel pipeline that synergistically balances high compression efficiency with the architectural demands of massively parallel hardware. We introduce a suite of targeted optimizations for computation, memory access, and GPU occupancy that enables GPZ to achieve near-hardware-limit throughput. We conduct an extensive evaluation on three distinct GPU architectures (workstation, data center, and edge) using six large-scale, real-world scientific datasets from five distinct domains. The results demonstrate that GPZ consistently and significantly outperforms five state-of-the-art GPU compressors, delivering up to 8x higher end-to-end throughput while simultaneously achieving superior compression ratios and data quality.
Source: arXiv (Cornell University)
Authors: Li, Ruoyu, Huang, Yafan, Zhang, Longtao, Yang, Zhuoxun, Di, Sheng, Huang, Jiajun, Liu, Jinyang, Tian, Jiannan, Liang, Xin, Li, Guanpeng, Guo, Hanqi, Cappello, Franck, Zhao, Kai
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6979299949
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