Many scientific applications opt for particles instead of meshes as their basic primitives to model complex systems composed of billions of discrete entities. Such applications span a diverse array of scientific domains, including molecular dynamics, cosmology, computational fluid dynamics, and geology. The scale of the particles in those scientific applications increases substantially thanks to the ever-increasing computational power in high-performance computing (HPC) platforms. However, the actual gains from such increases are often undercut by obstacles in data management systems related to data storage, transfer, and processing. Lossy compression has been widely recognized as a promising solution to enhance scientific data management systems regarding such challenges, although most existing compression solutions are tailored for Cartesian grids and thus have sub-optimal results on discrete particle data. In this paper, we introduce LCP, an innovative lossy compressor designed for particle datasets, offering superior compression quality and higher speed than existing compression solutions. Specifically, our contribution is threefold. (1) We propose LCP-S, an error-bound aware block-wise spatial compressor to efficiently reduce particle data size. This approach is universally applicable to particle data across various domains. (2) We develop LCP, a hybrid compression solution for multi-frame particle data, featuring dynamic method selection and parameter optimization. (3) We evaluate our solution alongside eight state-of-the-art alternatives on eight real-world particle datasets from seven distinct domains. The results demonstrate that our solution achieves up to 104% improvement in compression ratios and up to 593% increase in speed compared to the second-best option, under the same error criteria.

翻译：许多科学应用选择粒子而非网格作为基本图元，以模拟由数十亿离散实体构成的复杂系统。此类应用涵盖广泛的科学领域，包括分子动力学、宇宙学、计算流体力学和地质学。得益于高性能计算平台计算能力的持续提升，这些科学应用中的粒子规模显著增长。然而，数据存储、传输和处理等数据管理系统中的障碍往往削弱了这种增长带来的实际收益。有损压缩已被广泛认为是应对此类挑战、增强科学数据管理系统的有效方案，但现有压缩方法大多针对笛卡尔网格设计，对离散粒子数据的压缩效果欠佳。本文提出LCP——一种专为粒子数据集设计的创新型有损压缩器，在压缩质量和速度上均优于现有压缩方案。具体而言，我们的贡献包含三个方面：（1）提出LCP-S，一种基于误差感知的分块空间压缩器，可高效缩减粒子数据规模。该方法适用于各领域的粒子数据。（2）开发LCP混合压缩方案，针对多帧粒子数据实现动态方法选择与参数优化。（3）在来自七个不同领域的八个真实粒子数据集上，将我们的方案与八种先进替代方案进行对比评估。结果表明，在相同误差标准下，我们的方案相比次优方案最高可提升104%的压缩率，并实现最高593%的速度提升。