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Distributed Time-Dependent Contraction Hierarchies

机译:分布式时间相关收缩层次结构

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Server based route planning in road networks is now powerful enough to find quickest paths in a matter of milliseconds, even if detailed information on time-dependent travel times is taken into account. However this requires huge amounts of memory on each query server and hours of preprocessing even for a medium sized country like Germany. This is a problem since global internet companies would like to work with transcdntinental networks, detailed models of intersections, and regular re-preprocessing that takes the current traffic situation into account. By giving a distributed memory parallelization of the arguably best current technique - time-dependent contraction hierarchies, we remove these bottlenecks. For example, on a medium size network 64 processes accelerate preprocessing by a factor of 28 to 160 seconds, reduce per process memory consumption by a factor of 10.5 and increase query throughput by a factor of 25.
机译:道路网络中基于服务器的路线规划现在功能强大,即使考虑了与时间相关的行驶时间的详细信息,也可以在几毫秒内找到最快的路径。但是,即使对于像德国这样的中型国家,这也需要每个查询服务器上的大量内存和数小时的预处理。这是一个问题,因为全球互联网公司希望使用跨网网络,交叉口的详细模型以及考虑到当前交通状况的常规预处理。通过提供可以说是目前最好的技术(与时间有关的收缩层次结构)的分布式内存并行化,我们消除了这些瓶颈。例如,在中等规模的网络64上,进程将预处理速度提高了28到160秒,将每个进程的内存消耗降低了10.5倍,并将查询吞吐量提高了25倍。

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