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A multi-GPU accelerated solver for the three-dimensional two-phase incompressible Navier-Stokes equations

机译:三维两相不可压缩Navier-Stokes方程的多GPU加速求解器

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The use of graphics hardware for general purpose computations allows scientists to enormously speed up their numerical codes. We presently investigate the impact of this technology on our computational fluid dynamics solver for the three-dimensional two-phase incompressible Navier-Stokes equations, which is based on the level set technique and applies Chorin's projection approach. To our knowledge, this is the first time, that a two-phase solver for the Navier-Stokes equations profits from the computation power of modern graphics hardware. As part of our project, a Jacobi preconditioned conjugate gradient solver for the pressure Poisson equation and the reinitialization of the level set function of our CPU based code were ported to the graphics processing unit (GPU). They are implemented in double precision and parallelized by the Message Passing Interface (MPI). We obtain speedups of 16.2 and 8.6 for the Poisson solver and the reinitialization on one GPU in contrast to a single CPU. Our implementation scales close to perfect on multiple GPUs of a distributed memory cluster. This results in excellent speedups of 115.8 and 53.7 on eight GPUs of our cluster. Furthermore our whole multi-GPU accelerated solver achieves an impressive speedup of 69.6 on eight GPUs/CPUs.
机译:使用图形硬件进行通用计算可以使科学家极大地加快其数字编码的速度。我们目前正在研究此技术对基于二维水平不可压缩Navier-Stokes方程的计算流体动力学求解器的影响,该方程基于水平集技术并应用Chorin的投影方法。据我们所知,这是Navier-Stokes方程的两相求解器第一次受益于现代图形硬件的计算能力。作为我们项目的一部分,用于压力泊松方程的Jacobi预处理共轭梯度求解器和基于CPU的代码的水平集功能的重新初始化被移植到图形处理单元(GPU)。它们以双精度实现,并由消息传递接口(MPI)并行化。与单个CPU相比,我们在Poisson求解器上获得了16.2和8.6的加速,并且在一个GPU上进行了重新初始化。在分布式内存集群的多个GPU上,我们的实现规模接近完美。这样可以在集群的八个GPU上实现115.8和53.7的出色加速。此外,我们的整个多GPU加速求解器在八个GPU / CPU上的速度达到了惊人的69.6。

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