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Parallel implementation of the Finite-Difference Time-Domain method in Open Computing Language

机译:开放计算语言中有限差分时域方法的并行实现

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In this paper we evaluate the usability and performance of Open Computing Language (OpenCL) targeted for implementation of the Finite-Difference Time-Domain (FDTD) method. The simulation speed was compared to implementations based on alternative techniques of parallel processor programming. Moreover, the portability of OpenCL FDTD code between modern computing architectures was assessed. The average speed of OpenCL FDTD simulations on a GPU was about 1.1 times lower than a comparable CUDA based solver for domains with sizes varying from 503 to 4003 cells. Although OpenCL code dedicated to GPUs can be executed on multi-core CPUs, a direct porting does not provide satisfactory performance due to an application of architecture specific features in GPU code. Therefore, the OpenCL kernels of the developed FDTD code were optimized for multi-core CPUs. However, this improved OpenCL FDTD code was still about 1.5 to 2.5 times slower than the FDTD solver developed in the OpenMP parallel programming standard. The study concludes that, despite current performance drawbacks, the future potential of OpenCL is significant due to its flexibility and portability to various architectures.
机译:在本文中,我们评估了旨在实现时差有限域(FDTD)方法的开放计算语言(OpenCL)的可用性和性能。将仿真速度与基于并行处理器编程的替代技术的实现方式进行了比较。此外,评估了OpenCL FDTD代码在现代计算体系结构之间的可移植性。对于大小从50 3 到400 3 单元的域,在GPU上进行OpenCL FDTD仿真的平均速度比基于CUDA的同类求解器低约1.1倍。尽管专用于GPU的OpenCL代码可以在多核CPU上执行,但是由于在GPU代码中应用了特定于体系结构的功能,因此直接移植无法提供令人满意的性能。因此,已开发的FDTD代码的OpenCL内核针对多核CPU进行了优化。但是,这种改进的OpenCL FDTD代码仍然比OpenMP并行编程标准中开发的FDTD求解器慢约1.5到2.5倍。研究得出的结论是,尽管目前存在性能缺陷,但由于OpenCL的灵活性和可移植到各种体系结构的潜力,因此其潜力巨大。

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