Efficient parallel simulation over large-scale social contact networks
Social contact network (SCN) models the daily contacts between people in real life. It consists of agents and locations. When agents visit a location at the same time, the social interactions can be established among them. Simulations over SCN have been employed to study social dynamics such as dise...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/143058 |
| _version_ | 1811677643962056704 |
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| author | Wu, Yulin Cai, Wentong Li, Zengxiang Tan, Wen Jun Hou, Xiangting |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Wu, Yulin Cai, Wentong Li, Zengxiang Tan, Wen Jun Hou, Xiangting |
| author_sort | Wu, Yulin |
| collection | NTU |
| description | Social contact network (SCN) models the daily contacts between people in real life. It consists of agents and locations. When agents visit a location at the same time, the social interactions can be established among them. Simulations over SCN have been employed to study social dynamics such as disease spread among population. Because of the scale of SCN and the execution time requirement, the simulations are usually run in parallel. However, a challenge to the parallel simulation is that the structure of SCN is naturally skewed with a few hub locations that have far more visitors than others. These hub locations can cause load imbalance and heavy communication between partitions, which therefore impact the simulation performance. This article proposes a comprehensive solution to address this challenge. First, the hub locations are decomposed into small locations, so that SCN can be divided into partitions with better balanced workloads. Second, the agents are decomposed to exploit data locality, so that the overall communication across partitions can be greatly reduced. Third, two enhanced execution mechanisms are designed for locations and agents, respectively, to improve simulation parallelism. To evaluate the efficiency of the proposed solution, an epidemic simulation was developed and extensive experiments were conducted on two computer clusters using three SCN datasets with different scales. The results demonstrate that our approach can significantly improve the execution performance of the simulation. |
| first_indexed | 2024-10-01T02:40:38Z |
| format | Journal Article |
| id | ntu-10356/143058 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:40:38Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1430582020-07-24T06:23:06Z Efficient parallel simulation over large-scale social contact networks Wu, Yulin Cai, Wentong Li, Zengxiang Tan, Wen Jun Hou, Xiangting School of Computer Science and Engineering Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling Parallel and Distributed Simulation Skewed Degree Distribution Social contact network (SCN) models the daily contacts between people in real life. It consists of agents and locations. When agents visit a location at the same time, the social interactions can be established among them. Simulations over SCN have been employed to study social dynamics such as disease spread among population. Because of the scale of SCN and the execution time requirement, the simulations are usually run in parallel. However, a challenge to the parallel simulation is that the structure of SCN is naturally skewed with a few hub locations that have far more visitors than others. These hub locations can cause load imbalance and heavy communication between partitions, which therefore impact the simulation performance. This article proposes a comprehensive solution to address this challenge. First, the hub locations are decomposed into small locations, so that SCN can be divided into partitions with better balanced workloads. Second, the agents are decomposed to exploit data locality, so that the overall communication across partitions can be greatly reduced. Third, two enhanced execution mechanisms are designed for locations and agents, respectively, to improve simulation parallelism. To evaluate the efficiency of the proposed solution, an epidemic simulation was developed and extensive experiments were conducted on two computer clusters using three SCN datasets with different scales. The results demonstrate that our approach can significantly improve the execution performance of the simulation. Ministry of Education (MOE) National Research Foundation (NRF) Accepted version MOE Tier 1 Funding Yulin Wu and Wentong Cai acknowledge the funding support from the Singapore National Research Foundation under its Campus for Research Excellence And Technological Enterprise (CREATE) program. 2020-07-24T06:23:05Z 2020-07-24T06:23:05Z 2019 Journal Article Wu, Y., Cai, W., Li, Z., Tan, W. J., & Hou, X. (2019). Efficient parallel simulation over large-scale social contact networks. ACM Transactions on Modeling and Computer Simulation, 29(2), 10-. doi:10.1145/3265749 1049-3301 https://hdl.handle.net/10356/143058 10.1145/3265749 2-s2.0-85063189941 2 29 10:1 10:25 en ACM Transactions on Modeling and Computer Simulation © 2019 Association for Computing Machinery. All rights reserved. This paper was published in ACM Transactions on Modeling and Computer Simulation and is made available with permission of Association for Computing Machinery. application/pdf |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling Parallel and Distributed Simulation Skewed Degree Distribution Wu, Yulin Cai, Wentong Li, Zengxiang Tan, Wen Jun Hou, Xiangting Efficient parallel simulation over large-scale social contact networks |
| title | Efficient parallel simulation over large-scale social contact networks |
| title_full | Efficient parallel simulation over large-scale social contact networks |
| title_fullStr | Efficient parallel simulation over large-scale social contact networks |
| title_full_unstemmed | Efficient parallel simulation over large-scale social contact networks |
| title_short | Efficient parallel simulation over large-scale social contact networks |
| title_sort | efficient parallel simulation over large scale social contact networks |
| topic | Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling Parallel and Distributed Simulation Skewed Degree Distribution |
| url | https://hdl.handle.net/10356/143058 |
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