GPU Algorithms for Fastest Path Problem in Temporal Graphs
Pages 587 - 596
Abstract
This paper introduces the first gpu-based parallel algorithms to solve the fastest path duration (fpd) problem in temporal graphs. Fastest path duration in temporal graphs is a well studied problem that has multiple use cases in information diffusion, epidemic spreading, and route planning in public transportation. Given a temporal graph G in which each edge associates with a departure time and duration time, and a source vertex s, the Fastest Path Duration (fpd) problem is to compute the journey times from s to all the rest of the vertices in G. The existing multi-core algorithm for fpd by Delling et al. exhibits limited parallelism. In general, many parallel algorithms suffer from doing redundant work while pruning certain computations. Our research focuses on multiple algorithmic ways to avoid redundant work and perform pruning of computations effectively. We introduce three novel gpu-based parallel algorithms for fpd, namely Level Order (lo), Multiple Breadth First Search (mbfs), and Local Work-lists (lw) and implement them on a gpu architecture machine. Our algorithms demonstrate an average speedup of approximately 165 times and a maximum speedup of up to 1383 times over the current state-of-the-art algorithms. This paper provides a comprehensive explanation of various algorithm designs, their optimizations for gpus, and an extensive evaluation of their performance across various temporal graph scenarios.
References
[1]
Sara El Alaoui and Byrav Ramamurthy. 2017. EAODR: A novel routing algorithm based on the Modified Temporal Graph network model for DTN-based Interplanetary Networks. Comput. Networks 129 (2017), 129–141. https://doi.org/10.1016/J.COMNET.2017.09.012
[2]
Simon Bourigault, Cédric Lagnier, Sylvain Lamprier, Ludovic Denoyer, and Patrick Gallinari. 2014. Learning social network embeddings for predicting information diffusion. In Seventh ACM International Conference on Web Search and Data Mining, WSDM 2014, New York, NY, USA, February 24-28, 2014, Ben Carterette, Fernando Diaz, Carlos Castillo, and Donald Metzler (Eds.). ACM, NY, USA, 393–402. https://doi.org/10.1145/2556195.2556216
[3]
Rui Dai, Shenkun Xu, Qian Gu, Chenguang Ji, and Kaikui Liu. 2020. Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data. In KDD ’20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23-27, 2020, Rajesh Gupta, Yan Liu, Jiliang Tang, and B. Aditya Prakash (Eds.). ACM, CA, USA, 3074–3082. https://doi.org/10.1145/3394486.3403358
[4]
Daniel Delling, Bastian Katz, and Thomas Pajor. 2012. Parallel computation of best connections in public transportation networks. ACM J. Exp. Algorithmics 17, Article 4.4 (oct 2012), 26 pages. https://doi.org/10.1145/2133803.2345678
[5]
Radwa El Shawi, Joachim Gudmundsson, and Christos Levcopoulos. 2014. Quickest path queries on transportation network. Computational Geometry 47, 7 (2014), 695–709.
[6]
Sanaz Gheibi, Tania Banerjee, Sanjay Ranka, and Sartaj Sahni. 2021. An Effective Data Structure for Contact Sequence Temporal Graphs. In 2021 IEEE Symposium on Computers and Communications (ISCC). IEEE, Athens, Greece, 1–8. https://doi.org/10.1109/ISCC53001.2021.9631469
[7]
Sanaz Gheibi, Tania Banerjee, Sanjay Ranka, and Sartaj Sahni. 2024. Path Algorithms for Contact Sequence Temporal Graphs. Algorithms 17, 4 (2024), 1–19. https://doi.org/10.3390/a17040148
[8]
Chirayu Anant Haryan, G. Ramakrishna, Rupesh Nasre, and Allam Dinesh Reddy. 2020. A GPU Algorithm for Earliest Arrival Time Problem in Public Transport Networks. In 2020 IEEE 27th International Conference on High Performance Computing, Data, and Analytics (HiPC). IEEE, Virtual, 171–180. https://doi.org/10.1109/HiPC50609.2020.00031
[9]
Petter Holme and Jari Saramäki. 2012. Temporal networks. Physics reports 519, 3 (2012), 97–125.
[10]
Yushan Jiang, Shuteng Niu, Kai Zhang, Bowen Chen, Chengtao Xu, Dahai Liu, and Houbing Song. 2022. Spatial–Temporal Graph Data Mining for IoT-Enabled Air Mobility Prediction. IEEE Internet of Things Journal 9, 12 (2022), 9232–9240. https://doi.org/10.1109/JIOT.2021.3090265
[11]
Yue Jin, Zijun Chen, and Wenyuan Liu. 2024. Enumerating all multi-constrained s-t paths on temporal graph. Knowl. Inf. Syst. 66, 2 (2024), 1135–1165. https://doi.org/10.1007/S10115-023-01958-8
[12]
Hyoungshick Kim and Ross Anderson. 2012. Temporal node centrality in complex networks. Physical Review E 85, 2 (2012), 026107.
[13]
Youru Li, Zhenfeng Zhu, Xiaobo Guo, Linxun Chen, Zhouyin Wang, Yinmeng Wang, Bing Han, and Yao Zhao. 2023. Learning Joint Relational Co-evolution in Spatial-Temporal Knowledge Graph for SMEs Supply Chain Prediction. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, Long Beach, CA, USA, August 6-10, 2023, Ambuj K. Singh, Yizhou Sun, Leman Akoglu, Dimitrios Gunopulos, Xifeng Yan, Ravi Kumar, Fatma Ozcan, and Jieping Ye (Eds.). ACM, CA, USA, 4426–4436. https://doi.org/10.1145/3580305.3599855
[14]
Xiaoxiao Ma, Jia Wu, Shan Xue, Jian Yang, Chuan Zhou, Quan Z Sheng, Hui Xiong, and Leman Akoglu. 2021. A comprehensive survey on graph anomaly detection with deep learning. IEEE Transactions on Knowledge and Data Engineering 35, 12 (2021), 12012–12038.
[15]
Grzegorz Malewicz, Matthew H Austern, Aart JC Bik, James C Dehnert, Ilan Horn, Naty Leiser, and Grzegorz Czajkowski. 2010. Pregel: a system for large-scale graph processing. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data. ACM, Indianapolis, Indiana, USA, 135–146.
[16]
Junbin Mao, Hanhe Lin, Xu Tian, Yi Pan, and Jin Liu. 2023. FedGST: Federated Graph Spatio-Temporal Framework for Brain Functional Disease Prediction. In 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, Turkey, 1356–1361. https://doi.org/10.1109/BIBM58861.2023.10385983
[17]
Sunil Kumar Maurya and Anshu S Anand. 2022. A Novel GPU-Based Approach to Exploit Time-Respectingness in Public Transport Networks for Efficient Computation of Earliest Arrival Time. IEEE Access 10 (2022), 81877–81887.
[18]
Peng Ni, Masatoshi Hanai, Wen Jun Tan, Chen Wang, and Wentong Cai. 2017. Parallel algorithm for single-source earliest-arrival problem in temporal graphs. In 2017 46th International Conference on Parallel Processing (ICPP). IEEE, IEEE, Bristol, United Kingdom, 493–502.
[19]
Mithinti Srikanth and G. Ramakrishna. 2024. Efficient Algorithms for Earliest and Fastest Paths in Public Transport Networks. arxiv:2404.19422
[20]
Haoran Wang, Licheng Jiao, Fang Liu, Lingling Li, Xu Liu, Deyi Ji, and Weihao Gan. 2023. Learning Social Spatio-Temporal Relation Graph in the Wild and a Video Benchmark. IEEE Trans. Neural Networks Learn. Syst. 34, 6 (2023), 2951–2964. https://doi.org/10.1109/TNNLS.2021.3110682
[21]
Huanhuan Wu, James Cheng, Yiping Ke, Silu Huang, Yuzhen Huang, and Hejun Wu. 2016. Efficient Algorithms for Temporal Path Computation. IEEE Transactions on Knowledge and Data Engineering 28, 11 (2016), 2927–2942. https://doi.org/10.1109/TKDE.2016.2594065
[22]
Qiang Wu, Yuanze Du, Hua Xu, Yingwang Zhao, Xiaoyan Zhang, and Yi Yao. 2020. Finding the earliest arrival path through a time-varying network for evacuation planning of mine water inrush. Safety Science 130 (2020), 104836.
[23]
Philipp Zschoche, Till Fluschnik, Hendrik Molter, and Rolf Niedermeier. 2020. The complexity of finding small separators in temporal graphs. J. Comput. System Sci. 107 (2020), 72–92.
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- GPU Algorithms for Fastest Path Problem in Temporal Graphs
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Published In
August 2024
1279 pages
ISBN:9798400717932
DOI:10.1145/3673038
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Association for Computing Machinery
New York, NY, United States
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Published: 12 August 2024
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ICPP '24
ICPP '24: the 53rd International Conference on Parallel Processing
August 12 - 15, 2024
Gotland, Sweden
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Overall Acceptance Rate 91 of 313 submissions, 29%
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