The N-Queens problem plays an important role in academic research and practical application. Heuristic algorithm is often used to solve variant 2 of the N-Queens problem. In the process of solving, evaluation of the candidate solution, namely, fitness function, often occupies the vast majority of running time and becomes the key to improve speed. In this paper, three parallel schemes based on CPU and four parallel schemes based on GPU are proposed, and a serial scheme is implemented at the baseline. The experimental results show that, for a large-scale N-Queens problem, the coarse-grained GPU scheme achieved a maximum 307-fold speedup over a single-threaded CPU counterpart in evaluating a candidate solution. When the coarse-grained GPU scheme is applied to simulated annealing in solving N-Queens problem variant 2 with a problem size no more than 3000, the speedup is up to 9.3.

中文翻译：

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N-Queens问题的候选解评估算法的并行实现

N皇后问题在学术研究和实际应用中起着重要作用。启发式算法通常用于解决N-Queens问题的变体2。在求解过程中，对候选解（即适应度函数）的评估通常会占用绝大多数运行时间，并成为提高速度的关键。本文提出了三种基于CPU的并行方案和四种基于GPU的并行方案，并在基线处实现了串行方案。实验结果表明，对于大规模的N-Queens问题，在评估候选解决方案时，粗粒度GPU方案比单线程CPU方案获得了最大307倍的加速。