GPU-based Real-time Contact Tracing at Scale

Teng, Dejun; Nehe, Akshay; Emanuel, Prajeeth; Baig, Furqan; Kong, Jun; Wang, Fusheng

doi:10.1145/3474717.3483627

Proceedings of the 29th International Conference on Advances in Geographic Information Systems 2021

DOI: 10.1145/3474717.3483627

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GPU-based Real-time Contact Tracing at Scale

Dejun Teng

Akshay Nehe

Prajeeth Emanuel

et al.

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Cited by 2 publications

References 29 publications

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An urban crowd flow model integrating geographic characteristics

Zhang

Zhao

et al. 2023

Sci Rep

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Predicting urban crowd flow spatial distributions plays a critical role in optimizing urban public safety and traffic congestion management. The spatial dependency between regions and the temporal dynamics of the local crowd flow are two important features in urban crowd flow prediction. However, few studies considered geographic characteristic in terms of spatial features. To fill this gap, we propose an urban crowd flow prediction model integrating geographic characteristics (FPM-geo). First, three geographic characteristics, proximity, functional similarity, and road network connectivity, are fused by a residual multigraph convolution network to model the spatial dependency relationship. Then, a long short-term memory network is applied as a framework to integrate both the temporal dynamic patterns of local crowd flow and the spatial dependency between regions. A 4-day mobile phone dataset validates the effectiveness of the proposed method by comparing it with several widely used approaches. The result shows that the root mean square error decreases by 15.37% compared with those of the typical models with the prediction interval at the 15-min level. The prediction error increases with the crowd flow size in a local area. Moreover, the error reaches the top of the morning peak and the evening peak and slopes down to the bottom at night.

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An urban crowd flow model integrating geographic characteristics

Zhang

Zhao

et al. 2023

Sci Rep

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GTS: GPU-based Tree Index for Fast Similarity Search

Zhu,

Ma,

Zheng

et al. 2024

Proc. ACM Manag. Data

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Similarity search, the task of identifying objects most similar to a given query object under a specific metric, has gathered significant attention due to its practical applications. However, the absence of coordinate information to accelerate similarity search and the high computational cost of measuring object similarity hinder the efficiency of existing CPU-based methods. Additionally, these methods struggle to meet the demand for high throughput data management. To address these challenges, we propose GTS, a GPU-based tree index designed for the parallel processing of similarity search in general metric spaces, where only the distance metric for measuring object similarity is known. The GTS index utilizes a pivot-based tree structure to efficiently prune objects and employs list tables to facilitate GPU computing. To efficiently manage concurrent similarity queries with limited GPU memory, we have developed a two-stage search method that combines batch processing and sequential strategies to optimize memory usage. The paper also introduces an effective update strategy for the proposed GPU-based index, encompassing streaming data updates and batch data updates. Additionally, we present a cost model to evaluate search performance. Extensive experiments on five real-life datasets demonstrate that GTS achieves efficiency gains of up to two orders of magnitude over existing CPU baselines and up to 20x efficiency improvements compared to state-of-the-art GPU-based methods.

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GPU-based Real-time Contact Tracing at Scale

Cited by 2 publications

References 29 publications

An urban crowd flow model integrating geographic characteristics

An urban crowd flow model integrating geographic characteristics

GTS: GPU-based Tree Index for Fast Similarity Search

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