技术报告：GPU上时空轨迹数据库的并行距离阈值查询处理
Technical Report: Parallel Distance Threshold Query Processing for Spatiotemporal Trajectory Databases on the GPU
摘要：
处理移动对象的轨迹出现在许多应用领域，并已解决了从业者在时空数据库和地理信息系统社区。在这项工作中，我们专注于一个轨迹相似性搜索，距离阈值查询，它发现在给定的距离d内的搜索轨迹在一个时间间隔内的所有轨迹。我们展示了一个多线程实现的性能，其特点是使用一个R-树索引，并具有很高的并行效率（78%-90%）。我们介绍了一个GPGPU的实现，它避免了使用索引树，而是具有GPU友好的索引方法。我们比较多线程和GPU实现的性能，并表明，可以使用后者获得的加速比。我们提出了两类算法，SetSplit和GreedySetSplit，以创建高效的查询批次，减少内存压力和GPU上的计算成本。然而，我们发现使用固定大小的批次在实践中是足够有效的。我们开发了一个经验的性能模型，我们的GPGPU实现，可用于预测的响应时间的距离阈值查询。这个模型可以用来选择一个好的查询批量大小。
英文摘要：
Processing moving object trajectories arises in many application domains and has been addressed by practitioners in the spatiotemporal database and Geographical Information System communities. In this work, we focus on a trajectory similarity search, the distance threshold query, which finds all trajectories within a given distance d of a search trajectory over a time interval. We demonstrate the performance of a multithreaded implementation which features the use of an R-tree index and which has high parallel efficiency (78%-90%). We introduce a GPGPU implementation which avoids the use of index-trees, and instead features a GPU-friendly indexing method. We compare the performance of the multithreaded and GPU implementations, and show that a speedup can be obtained using the latter. We propose two classes of algorithms, SetSplit and GreedySetSplit, to create efficient query batches that reduce memory pressure and computational cost on the GPU. However, we find that using fixed-size batches is sufficiently efficient in practice. We develop an empirical performance model for our GPGPU implementation that can be used to predict the response time of the distance threshold query. This model can be used to pick a good query batch size.
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