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Performance improvements for calculations of third party risk around airports

机译:机场周围第三方风险计算的性能改进

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During the past two decades, NLR has been performing research for third party risk around airports, and a calculation model and methodology for different types of airports are developed. The NLR third party risk model is used to evaluate the risk for people living and working around an airport. The design of new or changed air routes and runway infrastructure at airports require that third party risk studies are conducted to determine the impact for the airport surroundings. Due to the increase of traffic, the availability of improved individual flight track data, and the need for detailed calculations on a denser grid, the time needed for a risk calculation has increased significantly. It becomes challenging when more scenarios are involved and hence more risk calculations are needed in the decision making with respect to airport development and land use planning. In this paper, we present the research of using parallel programming hardware to improve the performance of the calculation model, and in particular the use of graphics cards (GPUs) to carry out massive parallel operations. Because of different phases in the calculations, and the relation between adjacent grid cells, the translation of this model into a parallel implementation is not straightforward. Results show that a calculation for Schiphol airport, based on the traffic for a calendar year and a dense grid that took a week to complete in the original implementation, will finish well within one hour in the improved parallel implementation, with identical results. Other findings of the research show that the dedicated focus on improved performance has also helped finding and solving performance issues in the original model implementation.
机译:在过去的二十年中,NLR一直在对机场的第三方风险进行研究,并开发了不同类型机场的计算模型和方法。 NLR第三方风险模式用于评估人们生活和在机场工作的风险。机场的新的或改变的航线和跑道基础设施的设计要求进行第三方风险研究以确定机场环境的影响。由于流量的增加,改进的单独飞行跟踪数据的可用性,以及对更密集电网的详细计算的需要,风险计算所需的时间显着增加。当涉及更多场景时,它变得挑战,因此在机场开发和土地利用规划方面需要更多的风险计算。在本文中,我们介绍了使用并行编程硬件来提高计算模型的性能的研究,特别是使用图形卡(GPU)来执行大规模的并行操作。由于计算中的不同阶段,以及相邻网格单元之间的关系,该模型的翻译成并行实现并不直接。结果表明,基于日历年的交通和在原始实施中完成一周的浓密网格的基于交通,将在改进的平行实施中的一个小时内完成,在改进的平行实现中完成,结果良好,结果相同。研究结果表明,专注的关注改善性能也有助于在原始模型实施中找到和解决性能问题。

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