GPZ: GPU-Accelerated Lossy Compressor for Particle Data

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, 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.