基于GPU的粒子-粒子粒子-粒子树方法模拟稠密星团系统
GPU-Enabled Particle-Particle Particle-Tree Scheme for Simulating Dense Stellar Cluster System
摘要：
我们描述的${\rm P^3T}$（粒子树）计划模拟致密恒星系统的实现和性能。在${\rm P^3T}$中，粒子所受的力被分为短程和长程贡献。短程力的计算采用直接求和法，并采用块时间步长的四阶Hermite预估校正法进行积分。对于远程力，我们使用Barnes-Hut树代码和蛙跳积分器的组合。我们的模拟环境的树的一部分是加速使用图形处理单元（GPU），而直接求和是在主机CPU上进行。我们的代码提供了优良的性能和精度的星团模拟大量的粒子，即使当星团的核心尺寸很小。
英文摘要：
We describe the implementation and performance of the ${\rm P^3T}$ (Particle-Particle Particle-Tree) scheme for simulating dense stellar systems. In ${\rm P^3T}$, 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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