介绍如何在CUDA上搭建KD-TRIE，并对其进行搜索，使其能适应解决邻居搜索问题.实验结果表明，当搜索半径较小（如整个空间直径的0.01和0.001），数据规模较大（如10⁶）时，使用KD-TRIE进行搜索的效果最佳，与蛮力算法相比可以达到加速比5 000～15 000倍的效果；当搜索半径较大时，加速比会相应减少.采取优化措施,可以提高加速比.

This paper describes the construction of a KD-TRIE on CUDA, and the search and preprocessing for solving particle neighbor searching problem. The experimental results show that it is efficient to use KD-TRIE to search the CUDA when the search radius is not very large (e.g., 0.01 and 0.001 of the entire problem space diameter) and the data scale is relatively large (e.g., 10⁶). Compare to the brute force method, the speedup factor is about 5 000 to 15 000. However, if the search radius is too large, the speedup factor will drop. Various optimization methods discussed can be used to raise the speedup factor.