Algorithms for answering the k nearest-neighbor (k-NN) query are widely used for queries in spatial databases and for distance classification of a group of query points against a reference dataset to derive the dominating feature class. GPU devices have significantly more processing cores than CPUs and faster device memory than the main memory accessed by CPUs, thus, providing higher computing power for processing demanding queries like the k-NN. However, since device and/or main memory may not be able to host an entire, rather big, reference and query datasets, storing these datasets in a fast secondary device, like a solid state disk (SSD), and partially retrieve the required, at each stage, partitions is, in many practical cases, a feasible solution. We propose and implement the first GPU-based algorithms for processing the k-NN query for big reference and query spatial data stored on SSDs. Based on 3d synthetic and real big spatial data, we experimentally compare these algorithms and highlight the most efficient algorithmic variation. This variation utilizes a CUDA feature known as Concurrent Kernel Execution, to further improve its performance.

用于回答k最近邻 ( k -NN) 查询的算法广泛用于空间数据库中的查询以及针对参考数据集对一组查询点进行距离分类以导出主导要素类。GPU 设备比 CPU 拥有更多的处理核心，并且比 CPU 访问的主内存更快的设备内存，因此可以为处理要求较高的查询（例如 k）提供更高的计算能力。-NN。然而，由于设备和/或主存储器可能无法托管整个相当大的参考和查询数据集，因此将这些数据集存储在快速辅助设备（例如固态磁盘（SSD））中，并在每个阶段部分检索所需的数据，在许多实际情况下，分区是一种可行的解决方案。我们提出并实现了第一个基于 GPU 的算法，用于处理存储在 SSD 上的大参考和查询空间数据的k -NN 查询。基于 3D 合成和真实大空间数据，我们通过实验比较这些算法并突出最有效的算法变化。此变体利用称为并发内核执行的 CUDA 功能来进一步提高其性能。