首页> 外文会议>2012 SC Companion: High Performance Computing, Networking, Storage and Analysis. >Abstract: Scalable Fast Multipole Methods for Vortex Element Methods
【24h】

Abstract: Scalable Fast Multipole Methods for Vortex Element Methods

机译:摘要:涡旋元法的可扩展快速多极子方法

获取原文
获取原文并翻译 | 示例

摘要

We use a particle-based method to simulate incompressible flows, where the Fast Multipole Method (FMM) is used to accelerate the calculation of particle interactions. The most time-consuming kernelsâ"the Biot-Savart equation and stretching term of the vorticity equationâ"are mathematically reformulated so that only two Laplace scalar potentials are used instead of six, while automatically ensuring divergence-free far-field computation. Based on this formulation, and on our previous work for a scalar heterogeneous FMM algorithm, we develop a new FMM-based vortex method capable of simulating general flows including turbulence on heterogeneous architectures, which distributes the work between multi-core CPUs and GPUs to best utilize the hardware resources and achieve excellent scalability. The algorithm also uses new data structures which can dynamically manage inter-node communication and load balance efficiently but with only a small parallel construction overhead. This algorithm can scale to large-sized clusters showing both strong and weak scalability. Careful error and timing trade-off analysis are also performed for the cutoff functions induced by the vortex particle method. Our implementation can perform one time step of the velocity+stretching for one billion particles on 32 nodes in 55.9 seconds, which yields 49.12 Tflop/s.
机译:我们使用基于粒子的方法来模拟不可压缩的流动,其中使用快速多极方法(FMM)来加速粒子相互作用的计算。数学上最耗时的内核“ Biot-Savart方程和涡度方程的拉伸项”经过数学重构,因此仅使用两个Laplace标量势而不是六个,同时自动确保了无散度的远场计算。基于此公式,以及我们先前对标量异构FMM算法所做的工作,我们开发了一种新的基于FMM的涡旋方法,该方法能够在异构体系结构上模拟包括湍流在内的一般流程,从而将工作在多核CPU和GPU之间分配到最佳状态。利用硬件资源并实现出色的可扩展性。该算法还使用了新的数据结构,该结构可以动态管理节点间的通信并有效地进行负载平衡,但并行构建的开销很小。该算法可以扩展到大型群集,同时显示强和弱的可伸缩性。还对涡旋粒子法引起的截止函数进行了仔细的误差和时序权衡分析。我们的实现可以在55.9秒内对32个节点上的十亿个粒子执行速度+拉伸的一个时间步,从而产生49.12 Tflop / s。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号