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Exploiting GPUs to investigate an inversion method that retrieves cardiac conductivities from potential measurements

机译:利用GPU来研究一种反演方法,该方法可从潜在的测量结果中检索心脏电导率

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摘要

Accurate cardiac bidomain conductivity values are essential for realistic simulation of various cardiac electrophysiological phenomena. A method was previously developed that can determine the conductivities from measurements of potential on a multi-electrode array placed on the surface of the heart. These conductivities, as well as a value for fibre rotation, are determined using a mathematical model and a two-pass process that is based on Tikhonov regularisation. Using simulated potentials, to which noise is added, the inversion method was recently shown to retrieve the intracellular conductivities accurately with up to 15% noise and the extracellular conductivities extremely accurately even with 20% noise. Recent work investigated the sensitivity of the method to the choice of the regularisation parameters. Such a study only became possible due to modifications that were made to the C++ code so that it could run on graphical processing units (GPUs) on the CUDA platform. As the method required the solution of a large number of matrix equations, the highly parallel nature of GPUs was exploited to accelerate execution of the code. Reorganisation of the code and more efficient memory management techniques allowed the data to completely fit in the GPU memory. Comparison between the execution time on the GPU versus the original CPU code shows a speedup of up to 60 times. In the future, the speedup could be further increased with greater use of shared memory, which has a much lower latency (access time) than global memory.
机译:准确的心脏双畴电导率值对于各种心脏电生理现象的现实模拟至关重要。先前已开发出一种方法,该方法可以通过测量置于心脏表面的多电极阵列上的电势来确定电导率。这些电导率以及光纤旋转值是使用数学模型和基于Tikhonov正则化的两次通过过程确定的。使用模拟的电位并添加了噪声,最近证明了反演方法可以准确地检索到15%噪声的细胞内电导率,甚至精确地检索到20%噪声的细胞外电导率。最近的工作调查了该方法对正则化参数选择的敏感性。只有对C ++代码进行了修改,这样的研究才有可能实现,以便可以在CUDA平台上的图形处理单元(GPU)上运行。由于该方法需要解决大量矩阵方程式,因此利用了GPU的高度并行性来加速代码的执行。代码的重组和更有效的内存管理技术使数据完全适合GPU内存。 GPU上的执行时间与原始CPU代码之间的比较显示出最多60倍的加速。将来,可以通过更多使用共享内存来进一步提高速度,共享内存的延迟(访问时间)比全局内存低得多。

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