用于模拟密集星团系统的gpu支持粒子-粒子-粒子树方案

IF 16.281
Masaki Iwasawa, Simon Portegies Zwart, Junichiro Makino
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引用次数: 17

摘要

我们描述了用于模拟密集恒星系统的\(\mathrm {P}^{3}\mathrm{T}\) (Particle-Particle Particle-Tree)方案的实现和性能。在\(\mathrm{P}^{3}\mathrm{T}\)中,粒子所受的力分为短程力和长程力。用直接求和的方法求出短时力,并用四阶Hermite预测校正方法进行积分。对于远程力,我们使用巴恩斯-胡特树代码和跨越式积分器的组合。我们的模拟环境的树部分是使用图形处理单元(GPU)加速的,而直接求和是在主机CPU上进行的。我们的代码对于具有大量粒子的星团的模拟,即使在星团的核心尺寸很小的情况下,也具有出色的性能和准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

GPU-enabled particle-particle particle-tree scheme for simulating dense stellar cluster system

GPU-enabled particle-particle particle-tree scheme for simulating dense stellar cluster system

We describe the implementation and performance of the \(\mathrm {P}^{3}\mathrm{T}\) (Particle-Particle Particle-Tree) scheme for simulating dense stellar systems. In \(\mathrm{P}^{3}\mathrm{T}\), the force experienced by a particle is split into short-range and long-range contributions. Short-range forces are evaluated by direct summation and integrated with the fourth order Hermite predictor-corrector method with the block timesteps. For long-range forces, we use a combination of the Barnes-Hut tree code and the leapfrog integrator. The tree part of our simulation environment is accelerated using graphical processing units (GPU), whereas the direct summation is carried out on the host CPU. Our code gives excellent performance and accuracy for star cluster simulations with a large number of particles even when the core size of the star cluster is small.

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期刊介绍: Computational Astrophysics and Cosmology (CompAC) is now closed and no longer accepting submissions. However, we would like to assure you that Springer will maintain an archive of all articles published in CompAC, ensuring their accessibility through SpringerLink's comprehensive search functionality.
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