{"ID":2882131,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.10305","arxiv_id":"2508.10305","title":"GPZ: GPU-Accelerated Lossy Compressor for Particle Data","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.","short_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 com...","url_abs":"https://arxiv.org/abs/2508.10305","url_pdf":"https://arxiv.org/pdf/2508.10305v1","authors":"[\"Ruoyu Li\",\"Yafan Huang\",\"Longtao Zhang\",\"Zhuoxun Yang\",\"Sheng Di\",\"Jiajun Huang\",\"Jinyang Liu\",\"Jiannan Tian\",\"Xin Liang\",\"Guanpeng Li\",\"Hanqi Guo\",\"Franck Cappello\",\"Kai Zhao\"]","published":"2025-08-14T03:23:32Z","proceeding":"cs.DC","tasks":"[\"cs.DC\"]","methods":"[]","has_code":false}