GPZ: GPU-Accelerated Lossy Compressor for Particle Data
Ruoyu Li, Yafan Huang, Longtao Zhang, Zhuoxun Yang, Sheng Di, Jiajun Huang, Jinyang Liu, Jiannan Tian, Xin Liang, Guanpeng Li, Hanqi Guo, Franck Cappello, and Kai Zhao

TL;DR
GPZ is a GPU-optimized lossy compressor for particle data that achieves high throughput and better compression ratios by leveraging a novel parallel pipeline and targeted hardware optimizations.
Contribution
Introduces GPZ, a novel GPU-based lossy compressor with a four-stage pipeline and hardware-aware optimizations for large-scale particle datasets.
Findings
Up to 8x higher throughput than existing compressors
Superior compression ratios and data quality
Effective across diverse GPU architectures and datasets
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,…
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