基于CUDA的大规模通用粒子系统并行仿真

Xiangfei Li, Xuzhi Wang, W. Wan, Xiaoqiang Zhu, Xiaoqing Yu
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引用次数: 1

摘要

粒子系统最大的优点是非常适合在三维图形中建模复杂的模糊现象,如爆炸、喷泉、龙卷风和烟花等。随着人们对粒子数量和粒子间相互作用要求的不断提高,粒子系统模拟的计算复杂度迅速增加。粒子系统传统上是在通用CPU上实现的,粒子系统的计算复杂性限制了可以以交互速率计算的粒子数量。本文主要研究大尺度粒子系统的实时仿真。我们讨论了基于CUDA(计算统一设备架构)的可选集成算法,用于图形和科学模拟。粒子系统的速度已经大大提高,并行核gpu与多核cpu协同工作。为了提供可扩展、可移植的API库,采用面向对象的编程方法对并行粒子系统的功能进行封装。结果表明,我们提出的api用户友好,并行实现效率显著。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parallel Simulation of Large-Scale Universal Particle Systems Using CUDA
Particle systems' greatest advantage is well suited for modeling complex fuzzy phenomena, such as explosions, fountain, tornado and fireworks, etc. in 3D graphics. With the increasing requirements on the number of particles and particle-particle interactions, the computational complexity of simulation in particle systems has increased rapidly. Particle systems are traditionally implemented on a general-purpose CPU, and the computational complexity of particle systems limits the number of particles that can be computed at interactive rates. This paper focuses on real-time simulation of large-scale particle systems. We discuss optional integration algorithms based on CUDA (Compute Unified Device Architecture) for both graphic and scientific simulation. The speed of particle systems has been greatly improved, with parallel-core GPUs working in tandem with multi-core CPUs. In order to provide a scalable and portable API library, the object-oriented programming method is adopted to encapsulate the functions of parallel particle system. Results show that our proposed APIs are user-friendly and the parallel implementations are significantly efficient.
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