基于局部域分解的非线性k波仿真性能与精度分析

B. Treeby, Filip Vaverka, J. Jaros
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引用次数: 4

摘要

使用开源k-Wave工具箱进行大规模非线性超声模拟,现在通常使用MPI版本的k-Wave在传统的基于cpu的集群上运行。然而,3D快速傅立叶变换(FFT)所需的全对全通信在扩展到大量计算核心时严重影响性能。这可以通过使用基于局部傅里叶基的域分解策略来克服。在这项工作中,我们分析了在包含八个NVIDIA P40图形处理单元(gpu)的单个服务器上使用局部域分解在肾脏中运行高强度聚焦超声(HIFU)模拟的性能和准确性。研究了不同的分解和重叠大小,并与在使用1280核的基于cpu的超级计算机上运行的全局MPI模拟进行了比较。对于960 × 960 × 1280网格点的网格大小和4个网格点的重叠大小,与全局模拟相比,使用局部域分解的模拟误差约为0.1美元,这对于大多数应用来说已经足够了。运行模拟的财务成本也降低了一个数量级以上。使用开源k-Wave工具箱进行大规模非线性超声模拟,现在通常使用MPI版本的k-Wave在传统的基于cpu的集群上运行。然而,3D快速傅立叶变换(FFT)所需的全对全通信在扩展到大量计算核心时严重影响性能。这可以通过使用基于局部傅里叶基的域分解策略来克服。在这项工作中,我们分析了在包含八个NVIDIA P40图形处理单元(gpu)的单个服务器上使用局部域分解在肾脏中运行高强度聚焦超声(HIFU)模拟的性能和准确性。研究了不同的分解和重叠大小,并与在使用1280核的基于cpu的超级计算机上运行的全局MPI模拟进行了比较。当网格大小为960 × 960 × 1280网格点,重叠大小为4个网格点时,局部域分解模拟误差约为0.1…
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance and accuracy analysis of nonlinear k-Wave simulations using local domain decomposition with an 8-GPU server
Large-scale nonlinear ultrasound simulations using the open-source k-Wave toolbox are now routinely performed using the MPI version of k-Wave running on traditional CPU-based clusters. However, the all-to-all communications required by the 3D fast Fourier transform (FFT) severely impact performance when scaling to large numbers of compute cores. This can be overcome by using a domain decomposition strategy based on a local Fourier basis. In this work, we analyze the performance and accuracy of using local domain decomposition for running a high-intensity focused ultrasound (HIFU) simulation in the kidney on a single server containing eight NVIDIA P40 graphical processing units (GPUs). Different decompositions and overlap sizes are investigated and compared to a global MPI simulation running on a CPU-based supercomputer using 1280 cores. For a grid size of 960 by 960 by 1280 grid points and an overlap size of 4 grid points, the error in the simulation using local domain decomposition is on the order of 0.1$ compared to the global simulation, which is sufficient for most applications. The financial cost for running the simulation is also reduced by more than an order of magnitude.Large-scale nonlinear ultrasound simulations using the open-source k-Wave toolbox are now routinely performed using the MPI version of k-Wave running on traditional CPU-based clusters. However, the all-to-all communications required by the 3D fast Fourier transform (FFT) severely impact performance when scaling to large numbers of compute cores. This can be overcome by using a domain decomposition strategy based on a local Fourier basis. In this work, we analyze the performance and accuracy of using local domain decomposition for running a high-intensity focused ultrasound (HIFU) simulation in the kidney on a single server containing eight NVIDIA P40 graphical processing units (GPUs). Different decompositions and overlap sizes are investigated and compared to a global MPI simulation running on a CPU-based supercomputer using 1280 cores. For a grid size of 960 by 960 by 1280 grid points and an overlap size of 4 grid points, the error in the simulation using local domain decomposition is on the order of 0.1...
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