基于 GPU 的大规模实时联系人追踪。

Dejun Teng, Akshay Nehe, Prajeeth Emanuel, Furqan Baig, Jun Kong, Fusheng Wang
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引用次数: 0

摘要

接触追踪对于控制 COVID-19 的传播越来越重要。然而,频繁采样的大量追踪数据给实时处理带来了巨大挑战。在本文中,我们提出了一种基于 GPU 的实时接触追踪系统,该系统基于使用位置数据的带有时间约束的空间接近性查询。我们在 GPU 上使用自适应分区模式为移动物体提供动态索引,而且开销极低。我们的系统优化了接触对的检索,以同时满足接触追踪场景和以 GPU 为中心的并行性的要求。我们提出了一种高效的接触评估机制,只保留空间和时间上有效的接触。我们的实验证明,该系统可以在数千万人的大规模联系人追踪中实现亚秒级响应,与基于 CPU 的方法相比,性能提升了两个量级。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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GPU-based Real-time Contact Tracing at Scale.

Contact tracing is gaining its importance in controlling the spread of COVID-19. However, the enormous volume of the frequently sampled tracing data brings major challenges for real-time processing. In this paper, we propose a GPU-based real-time contact tracing system based on spatial proximity queries with temporal constraints using location data. We provide dynamic indexing of moving objects using an adaptive partitioning schema on GPU with extremely low overhead. Our system optimizes the retrieval of contacted pairs to match both the requirements of contact tracing scenarios and GPU centered parallelism. We propose an efficient contacts evaluation mechanism to keep only the spatially and temporally valid contacts. Our experiments demonstrate that the system can achieve sub-second level response for large-scale contact tracing of tens of millions of people, with two magnitudes of performance boost over CPU based approach.

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Hide Your Distance: Privacy Risks and Protection in Spatial Accessibility Analysis. GPU-based Real-time Contact Tracing at Scale. Mining Public Datasets for Modeling Intra-City PM2.5 Concentrations at a Fine Spatial Resolution. SparkGIS: Resource Aware Efficient In-Memory Spatial Query Processing. iSPEED: an Efficient In-Memory Based Spatial Query System for Large-Scale 3D Data with Complex Structures.
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