N‐Queens problem derives three variants: obtaining a specific solution, obtaining a set of solutions and obtaining all solutions. The purpose of the variant I is to find a constructive solution, which has been solved. Variant III is aiming to find all solutions and the largest number of queens currently being resolved is 26. Variant II whose purpose is to obtain a set of solutions for larger‐scale problems relies on various intelligent algorithms. In this paper, we use a master‐slave model genetic algorithm that combines the idea of the evolutionary algorithm and simulated annealing algorithm to solve Variant III, and use a parallel fitness function based on compute unified device architecture. Experimental results show that our scheme achieved a maximum 60‐fold speedup over the single‐CPU counterpart. On this basis, a two‐level parallel genetic algorithm based on the island model and master‐slave model is implemented on the GPU cluster by using message passing interface technology. Using two‐node and three‐node GPU cluster, speedup of 1.46 and 2.01 are obtained on average over single‐node, respectively. Compared with the sequential genetic algorithm, the two‐level parallel genetic algorithm makes full use of the parallel computing power of GPU cluster in solving N‐Queen variant II and improves the performance by 99.19 times in the best case.

中文翻译：

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基于消息传递接口-计算统一设备架构的N-Queens问题并行遗传算法

N皇后问题衍生出三个变体：获得特定的解决方案，获得一组解决方案以及获得所有解决方案。变体I的目的是找到一个建设性的解决方案，该解决方案已得到解决。变式III旨在找到所有解决方案，目前正在解决的皇后数量最多，为26。变式II的目的是获得针对更大范围问题的一组解决方案，它依赖于各种智能算法。在本文中，我们使用主从模型遗传算法，结合进化算法和模拟退火算法的思想来求解变体III，并使用基于计算统一设备架构的并行适应度函数。实验结果表明，我们的方案比单CPU方案达到了最大60倍的加速。在此基础上，通过使用消息传递接口技术，在GPU集群上实现了基于岛模型和主从模型的两级并行遗传算法。使用2节点和3节点GPU集群，单节点上的平均速度分别为1.46和2.01。与顺序遗传算法相比，二级并行遗传算法在解决N-Queen变体II时充分利用了GPU集群的并行计算能力，在最佳情况下将性能提高了99.19倍。