GPU accelerated FDTD based Open-source SAR Simulator

Vidhi Katkoria, Pratik Ghosh, B. Chaudhury
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Abstract

Specific Absorption Rate (SAR) is a widely used metric to measure the amount of non-ionizing EM radiation power absorbed by a unit mass of biological tissue, thus it helps in determining the safety standard of any EM applications. The 3D Finite Difference Time Domain (FDTD) is a commonly used computational technique for accurate SAR computation. However, it is computationally as well as memory intensive and requires high-performance computing(HPC) clusters to handle problems of large sizes encountered during high-frequency applications. This paper describes the implementation of a fast, optimized Open Source GPU accelerated FDTD based SAR calculator (available at Github)using CUDA (Code Unified Device Architecture) as an alternative that can be developed locally at a relatively small cost. On a testbed comprising a serial implementation on Intel i7-4790 CPU and parallel implementations on NVIDIA Tesla K40 graphics card, we have achieved a speedup of up to 45x in double precision mode. We have investigated the effect of increasing the frequency (larger problem sizes) and threads per block on speedup. Finally, we have compared the performance of our parallel implementation on two different GPUs (Tesla K40 and GTX 670) and analyzed the effect of hardware architecture on the performance of the SAR calculator.
基于GPU加速FDTD的开源SAR模拟器
比吸收率(SAR)是一种广泛使用的度量,用于测量单位质量生物组织吸收的非电离电磁辐射功率的量,因此它有助于确定任何电磁应用的安全标准。三维时域有限差分(FDTD)是一种常用的精确SAR计算技术。然而,它是计算和内存密集型的,并且需要高性能计算(HPC)集群来处理在高频应用中遇到的大规模问题。本文描述了一个快速,优化的开源GPU加速基于FDTD的SAR计算器(可在Github上获得)的实现,使用CUDA(代码统一设备架构)作为替代方案,可以以相对较小的成本在本地开发。在由Intel i7-4790 CPU串行实现和NVIDIA Tesla K40显卡并行实现组成的测试平台上,我们在双精度模式下实现了高达45倍的加速。我们已经研究了增加频率(更大的问题大小)和每个块的线程数对加速的影响。最后,我们比较了我们的并行实现在两种不同gpu (Tesla K40和GTX 670)上的性能,并分析了硬件架构对SAR计算器性能的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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