Abstract
The simulation of ultrasound wave propagation through biological tissue has a wide range of practical applications. However, large grid sizes are generally needed to capture the phenomena of interest. Here, a novel approach to reduce the computational complexity is presented. The model uses an accelerated k -space pseudospectral method which enables more than one hundred GPUs to be exploited to solve problems with more than 3*10^9 grid points. The classic communication bottleneck of Fourier spectral methods, all-to-all global data exchange, is overcome by the application of domain decomposition using local Fourier basis. Compared to global domain decomposition, for a grid size of 1536 x 1024 x 2048, this reduces the simulation time by a factor of 7.5 and the simulation cost by a factor of 3.8.
Highlights
Simulating the propagation of ultrasound waves through biological tissue has a large number of practical applications, including the physics-based simulation of diagnostic ultrasound images [1] and treatment planning for ultrasound therapy [2]
As error correction code (ECC) is switched on, the on-board memory capacity is reduced to approximately 5.4 GB
The graphics processing units (GPUs) are grouped in configurations of 3 or 8 per node, connected by PCI-Express 2.0
Summary
Simulating the propagation of ultrasound waves through biological tissue has a large number of practical applications, including the physics-based simulation of diagnostic ultrasound images [1] and treatment planning for ultrasound therapy [2] (a more comprehensive list is provided in [3]). Ultrasound simulation for these applications is computationally demanding due to the length scales involved, where the propagation length can be hundreds or thousands of times longer than the acoustic wavelength This leads to very large domain sizes, in some cases with more than 100 billion grid points [2]. One of the biggest challenges in performing large-scale ultrasound simulations is the accumulation of numerical dispersion This can be overcome through the application of spectral methods, which can be considered memory minimising due to their exponential error convergence with grid density [5]. This combines the spectral calculation of spatial gradients (using the Fourier collocation spectral method) with a dispersion-corrected finite difference scheme to integrate forward in time. For the k-space method, the FFTs are 3D, as the dispersion correction step is applied in the spatial Fourier domain
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