Parallel Implementation of the BiCGStab(2) Method in GPU Using CUDA and Matlab for Solution of Linear Systems |
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作者姓名: | Lauro Cassio Martins de Paula Anderson da Silva Soares |
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作者单位: | Institute of lnformatics, Federal University of Goias (UFG), Goias 74001-970, Brazil |
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基金项目: | Acknowledgment The authors thank the research agencies CAPES and FAPEG for the support provided to this research |
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摘 要: | This paper presents a parallel implementation of the hybrid BiCGStab(2) (bi-conjugate gradient stabilized) iterative method in a GPU (graphics processing unit) for solution of large and sparse linear systems. This implementation uses the CUDA-Matlab integration, in which the method operations are performed in a GPU core using Matlab built-in functions. The goal is to show that the exploitation of parallelism by using this new technology can provide a significant computational performance. For the validation of the work, we compared the proposed implementation with a BiCGStab(2) sequential and parallelized implementation in the C and CUDA-C languages. The results showed that the proposed implementation is more efficient and can be viable for simulations being carried out with quality and in a timely manner. The gains in computational efficiency were 76x and 6x compared to the implementation in C and CUDA-C, respectively.
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关 键 词: | Matlab 并行实现 线性系统 GPU 图形处理单元 双共轭梯度 内置函数 计算性能 |
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