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Parallel Genetic Algorithm using Compute Unified Device Architecture (CUDA) for Designing Energy Saving Glass Coating Structure

机译:基于计算统一设备架构(CUDA)的并行遗传算法设计节能玻璃涂层结构

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Parallel Processing is proven to provide faster speed in generating the chromosome of the coating structure of Energy saving glass. The technique that has been used is parallel genetic algorithm, since parallel genetic algorithm require high speed of CPU in order to generate the result, yet high performance of CPU will require additional cost, with the use of GPU it is proven that the speed of the GPU is 100 times faster than the CPU which can shorten the duration in generating the code and result. The result will be in binary format which is the best chromosome that will be selected used as the design for the coating structure. This paper will cover the study of GPU and CUDA platform and analyze the result of the coating. The simulation result of the coating structure will be compare with different glass width or border and different frequency with same material and same coating structure design. This paper will discuss on the coating structure based on the simulation result whether the glass width can affect the signal frequency and which type of application that suitable with it. Speed test will also cover in this paper to prove that parallel processing does shorten the time of processing.
机译:实践证明,平行加工可加快生成节能玻璃涂层结构染色体的速度。已经使用的技术是并行遗传算法,因为并行遗传算法需要高速的CPU才能生成结果,而高性能的CPU则需要额外的成本,而使用GPU证明了并行处理器的速度。 GPU比CPU快100倍,可以缩短生成代码和结果的时间。结果将是二进制格式,这是将被选作涂层结构设计的最佳染色体。本文将涵盖GPU和CUDA平台的研究,并分析涂层的结果。在相同材料和相同涂层结构设计的情况下,将涂层结构的仿真结果与不同的玻璃宽度或边界以及不同的频率进行比较。本文将基于模拟结果来讨论涂层结构,玻璃宽度是否会影响信号频率以及哪种类型的应用适合它。速度测试还将在本文中进行介绍,以证明并行处理的确缩短了处理时间。

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