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.

中文翻译：

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支持GPU的粒子-粒子粒子树方案用于模拟密集恒星集群系统

我们描述了用于模拟密集恒星系统的$ \ mathrm {P} ^ {3} \ mathrm {T} $（粒子-粒子粒子树）方案的实现和性能。在$ \ mathrm {P} ^ {3} \ mathrm {T} $中，粒子经历的力分为短距离和长距离贡献。短程力通过直接求和来评估，并与具有块时间步长的四阶Hermite预测器-校正器方法集成在一起。对于远程部队，我们结合使用Barnes-Hut树代码和跳越积分器。我们的仿真环境的树状部分使用图形处理单元（GPU）进行了加速，而直接求和则在主机CPU上进行。