Multisensor data fusion with Dempster–Shafer (D–S) theory is beneficial for context inference without any advanced information. D–S theory includes reasoning based on Dempster’s rule of combination of degrees of belief based on different pieces of evidence. Although, the computational complexity of Dempster’s rule of combination is enormous. Any change in the number of pieces of evidence or hypotheses causes a lot of additional computations. Dempster’s rule of combination is shown to be #P-complete. The combination rule of $k$ frames of evidence with $n_k$ elements, such that ($k\in \mathbb{N}|k\ge 2$) has a time complexity of $O\left(2^N\right)$, where $N={\sum }_k{n}_k$. In this paper, we propose a parallel computing approach for Dempster’s rule of combination based on the concept of conquer and divide algorithms. The proportion of task benefiting from improvement is $p=1-\frac{2}{k}$, hence $\frac{k}{2}$ of theoretical maximum speed-up according to Amdahl’s law. We have tested our algorithm in different experimental settings and observed that the new parallel computing approach has not only achieved the best results in CPU version, but has also outperformed GPU version using Thrust CUDA in almost scenarios of the experiment.

带有Dempster-Shafer（DS）理论的多传感器数据融合对于无需任何高级信息的上下文推断是有利的。D–S理论包括基于Dempster基于不同证据的信念度组合规则的推理。虽然，Dempster组合规则的计算复杂度很高。证据或假设的数量上的任何变化都会导致大量额外的计算。Dempster的组合规则显示为#P完全。的组合规则$ķ$ 证据框架 ${ñ}_{ķ}$ 元素，例如（$ķ\in \mathbb{ñ}|ķ\ge 2$）的时间复杂度为 $Ø（2^{ñ}）$，在哪里 $ñ={\sum }_{ķ}{ñ}_{ķ}$。在本文中，我们基于征服和除法算法的概念，提出了一种用于Dempster组合规则的并行计算方法。受益于改进的任务比例为$p=\mathrm{1个}-\frac{2}{ķ}$，因此 $\frac{ķ}{2}$根据阿姆达尔定律的理论上最大提速。我们已经在不同的实验设置下测试了算法，并观察到新的并行计算方法不仅在CPU版本上获得了最佳结果，而且在实验的几乎所有场景中使用Thrust CUDA的性能都超过了GPU版本。