Accelerated DEVS Simulation Using Collaborative Computation on Multi-Cores and GPUs for Fire-Spreading IoT Sensing Applications
Discrete event system specification (DEVS) has been widely used in event-driven simulations for sensor-driven Internet of things (IoT) applications, such as monitoring the spread of fire disaster. Event-driven models for IoT sensor nodes and their communication is described in DEVS and they have to...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-08-01
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| Series: | Applied Sciences |
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| Online Access: | http://www.mdpi.com/2076-3417/8/9/1466 |
| _version_ | 1819062268658188288 |
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| author | Seongseop Kim Jeonghun Cho Daejin Park |
| author_facet | Seongseop Kim Jeonghun Cho Daejin Park |
| author_sort | Seongseop Kim |
| collection | DOAJ |
| description | Discrete event system specification (DEVS) has been widely used in event-driven simulations for sensor-driven Internet of things (IoT) applications, such as monitoring the spread of fire disaster. Event-driven models for IoT sensor nodes and their communication is described in DEVS and they have to be integrated with continuous models of fire-spreading dynamics so that the hybrid system modeling and simulation approach have to be considered for both continuous behavior of fire-spreading and event-driven communications by large-scale IoT sensor devices. The hybrid-integrated modelling and simulation for fire-spreading in wide area and large-scale IoT devices result in more complex model evaluation, including simulation time synchronization, so that simulation acceleration is important by considering scalability in large-scale IoT-driven applications that sense fire-spreading. In this study, we proposed a scalable simulation acceleration of a DEVS-based hybrid system using heterogeneous architecture based on multi-cores and graphic processing units (GPUs). We evaluated the power consumption comparison of the proposed accelerated-simulation approach in terms of the composition of the event-driven IoT models and continuous fire-spreading models, which are tightly described in differential equations across a large number of cellular models. The demonstrated result shows that the full utilization of CPU-GPU integrated computing resources, on which event-driven models and continuous models are efficiently deployed and optimally distributed, could enable an advantage for high-performance simulation speedup in terms of execution time, although more power consumption is required, but the total energy consumption could be reduced due to fast simulation time. |
| first_indexed | 2024-12-21T14:56:05Z |
| format | Article |
| id | doaj.art-8bb9172559f541e5b0ee6d7d056debd4 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-21T14:56:05Z |
| publishDate | 2018-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8bb9172559f541e5b0ee6d7d056debd42022-12-21T18:59:44ZengMDPI AGApplied Sciences2076-34172018-08-0189146610.3390/app8091466app8091466Accelerated DEVS Simulation Using Collaborative Computation on Multi-Cores and GPUs for Fire-Spreading IoT Sensing ApplicationsSeongseop Kim0Jeonghun Cho1Daejin Park2School of Electronics Engineering, Kyungpook National University, Daegu 41566, KoreaSchool of Electronics Engineering, Kyungpook National University, Daegu 41566, KoreaSchool of Electronics Engineering, Kyungpook National University, Daegu 41566, KoreaDiscrete event system specification (DEVS) has been widely used in event-driven simulations for sensor-driven Internet of things (IoT) applications, such as monitoring the spread of fire disaster. Event-driven models for IoT sensor nodes and their communication is described in DEVS and they have to be integrated with continuous models of fire-spreading dynamics so that the hybrid system modeling and simulation approach have to be considered for both continuous behavior of fire-spreading and event-driven communications by large-scale IoT sensor devices. The hybrid-integrated modelling and simulation for fire-spreading in wide area and large-scale IoT devices result in more complex model evaluation, including simulation time synchronization, so that simulation acceleration is important by considering scalability in large-scale IoT-driven applications that sense fire-spreading. In this study, we proposed a scalable simulation acceleration of a DEVS-based hybrid system using heterogeneous architecture based on multi-cores and graphic processing units (GPUs). We evaluated the power consumption comparison of the proposed accelerated-simulation approach in terms of the composition of the event-driven IoT models and continuous fire-spreading models, which are tightly described in differential equations across a large number of cellular models. The demonstrated result shows that the full utilization of CPU-GPU integrated computing resources, on which event-driven models and continuous models are efficiently deployed and optimally distributed, could enable an advantage for high-performance simulation speedup in terms of execution time, although more power consumption is required, but the total energy consumption could be reduced due to fast simulation time.http://www.mdpi.com/2076-3417/8/9/1466discrete event simulationhybrid simulationGPU-based accelerated simulationsimulation kernel distribution on multi-core architecture |
| spellingShingle | Seongseop Kim Jeonghun Cho Daejin Park Accelerated DEVS Simulation Using Collaborative Computation on Multi-Cores and GPUs for Fire-Spreading IoT Sensing Applications Applied Sciences discrete event simulation hybrid simulation GPU-based accelerated simulation simulation kernel distribution on multi-core architecture |
| title | Accelerated DEVS Simulation Using Collaborative Computation on Multi-Cores and GPUs for Fire-Spreading IoT Sensing Applications |
| title_full | Accelerated DEVS Simulation Using Collaborative Computation on Multi-Cores and GPUs for Fire-Spreading IoT Sensing Applications |
| title_fullStr | Accelerated DEVS Simulation Using Collaborative Computation on Multi-Cores and GPUs for Fire-Spreading IoT Sensing Applications |
| title_full_unstemmed | Accelerated DEVS Simulation Using Collaborative Computation on Multi-Cores and GPUs for Fire-Spreading IoT Sensing Applications |
| title_short | Accelerated DEVS Simulation Using Collaborative Computation on Multi-Cores and GPUs for Fire-Spreading IoT Sensing Applications |
| title_sort | accelerated devs simulation using collaborative computation on multi cores and gpus for fire spreading iot sensing applications |
| topic | discrete event simulation hybrid simulation GPU-based accelerated simulation simulation kernel distribution on multi-core architecture |
| url | http://www.mdpi.com/2076-3417/8/9/1466 |
| work_keys_str_mv | AT seongseopkim accelerateddevssimulationusingcollaborativecomputationonmulticoresandgpusforfirespreadingiotsensingapplications AT jeonghuncho accelerateddevssimulationusingcollaborativecomputationonmulticoresandgpusforfirespreadingiotsensingapplications AT daejinpark accelerateddevssimulationusingcollaborativecomputationonmulticoresandgpusforfirespreadingiotsensingapplications |