GPZ: GPU-Accelerated Lossy Compressor for Particle Data
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909736700477440 |
|---|---|
| author | 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 |
| author_facet | 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10305 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GPZ: GPU-Accelerated Lossy Compressor for Particle Data 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 Distributed, Parallel, and Cluster Computing 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. |
| title | GPZ: GPU-Accelerated Lossy Compressor for Particle Data |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2508.10305 |