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首页> 外文期刊>Applied Computational Electromagnetics Society journal >Using MATLAB’s Parallel Processing Toolbox for Multi-CPU and Multi-GPU Accelerated FDTD Simulations
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Using MATLAB’s Parallel Processing Toolbox for Multi-CPU and Multi-GPU Accelerated FDTD Simulations

机译:使用MATLAB的并行处理工具箱用于多CPU和多GPU加速FDTD模拟

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摘要

MATLAB is a good testbed for prototyping new FDTD techniques as it provides quick programming, debugging and visualization capabilities compared to lower level languages such as C or FORTRAN. However, the major disadvantage of using MATLAB is its slow execution. For faster simulations, one should use other programming languages like Fortran or C with CUDA when utilizing graphics processing units. Development of simulation codes using these other programming languages is not as easy as when using MATLAB. Thus the main objective of this paper is to investigate ways to increase the throughput of a fully functional finite difference time domain method coded in MATLAB to be able to simulate practical problems with visualization capabilities in reasonable time. We present simple ways to improve the efficiency of MATLAB simulations using the parallel toolbox along with the multi-core central processing units (CPUs) or the multiple graphics processing units (GPUs). Native and simple MATLAB constructs with no external dependencies or libraries and no expensive or complicated hardware acceleration units are used in the present development.
机译:MATLAB是一个很好的测试平台,用于原型设计新的FDTD技术,因为它提供了与C或FORTRAN等较低级别语言相比的快速编程,调试和可视化功能。然而,使用MATLAB的主要缺点是其执行缓慢。为了更快的模拟,在利用图形处理单元时,应该使用其他编程语言如FORTRAN或C。使用这些其他编程语言的仿真代码的开发并不像使用MATLAB时那么容易。因此,本文的主要目的是调查增加在Matlab中编码的全功能有限差分时间域方法的吞吐量,以便能够在合理的时间内模拟可视化功能的实际问题。我们介绍了使用并行工具箱以及多核中央处理单元(CPU)或多个图形处理单元(GPU)的MATLAB模拟效率的简单方法。本机和简单的Matlab构造没有外部依赖项或库,并且在当前开发中使用昂贵或复杂的硬件加速单元。

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