Parallel simulation of cyber-physical systems

Abstract Model-based design (MBD) in systems engineering is a well-accepted technique to abstract, analyze, verify, and validate complex systems. In MBD, we design a mathematical model of the system to virtually execute and test systems via model simulations to understand the system dynamics better....
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Das, Kamal [verfasserIn] Gurung, Amit [verfasserIn] Ray, Rajarshi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Hybrid systems Hybrid automaton Simulation trajectory Parallel simulations

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: Innovations in systems and software engineering - London : Springer, 2005, 17(2021), 3 vom: 01. Apr., Seite 319-331
Übergeordnetes Werk:	volume:17 ; year:2021 ; number:3 ; day:01 ; month:04 ; pages:319-331

Links:	Volltext

DOI / URN:	10.1007/s11334-021-00391-w

Katalog-ID:	SPR044715323

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520			\|a Abstract Model-based design (MBD) in systems engineering is a well-accepted technique to abstract, analyze, verify, and validate complex systems. In MBD, we design a mathematical model of the system to virtually execute and test systems via model simulations to understand the system dynamics better. Computing model simulations has their challenges; one is to ensure that the simulation trajectory preserves the model semantics. Besides, computing many simulation trajectories over a long time-horizon must be time efficient for rapid respond to system engineers. In this work, we address these challenges in simulating models of cyber-physical systems (CPS), particularly systems possessing mixed discrete–continuous dynamics. We focus on the subclass of CPS’s hybrid automata models, where Jump predicates are restricted to polygonal constraints and present a numerical simulation engine that can efficiently compute many random simulations in parallel by exploiting the parallel computing capability in modern multicore processors. Our simulation engine implements a lock-free parallel breadth-first-search (BFS)-like algorithm and is implemented in the model-checking tool XSpeed. In addition, an application of our simulation engine in property verification of CPS models has been illustrated on two benchmarks. Some model coverage metrics have been defined that users of the tool can specify to set the desired thoroughness of testing with simulations. We demonstrate the performance gains of our simulation engine over SpaceEx and CORA, the modern model checkers and simulators for affine hybrid systems.
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912			\|a GBV_ILN_152
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
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912			\|a GBV_ILN_213
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912			\|a GBV_ILN_250
912			\|a GBV_ILN_281
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912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
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912			\|a GBV_ILN_2044
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912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2107
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
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allfields	10.1007/s11334-021-00391-w doi (DE-627)SPR044715323 (SPR)s11334-021-00391-w-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl Das, Kamal verfasserin aut Parallel simulation of cyber-physical systems 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract Model-based design (MBD) in systems engineering is a well-accepted technique to abstract, analyze, verify, and validate complex systems. In MBD, we design a mathematical model of the system to virtually execute and test systems via model simulations to understand the system dynamics better. Computing model simulations has their challenges; one is to ensure that the simulation trajectory preserves the model semantics. Besides, computing many simulation trajectories over a long time-horizon must be time efficient for rapid respond to system engineers. In this work, we address these challenges in simulating models of cyber-physical systems (CPS), particularly systems possessing mixed discrete–continuous dynamics. We focus on the subclass of CPS’s hybrid automata models, where Jump predicates are restricted to polygonal constraints and present a numerical simulation engine that can efficiently compute many random simulations in parallel by exploiting the parallel computing capability in modern multicore processors. Our simulation engine implements a lock-free parallel breadth-first-search (BFS)-like algorithm and is implemented in the model-checking tool XSpeed. In addition, an application of our simulation engine in property verification of CPS models has been illustrated on two benchmarks. Some model coverage metrics have been defined that users of the tool can specify to set the desired thoroughness of testing with simulations. We demonstrate the performance gains of our simulation engine over SpaceEx and CORA, the modern model checkers and simulators for affine hybrid systems. Hybrid systems (dpeaa)DE-He213 Hybrid automaton (dpeaa)DE-He213 Simulation trajectory (dpeaa)DE-He213 Parallel simulations (dpeaa)DE-He213 Gurung, Amit verfasserin aut Ray, Rajarshi verfasserin aut Enthalten in Innovations in systems and software engineering London : Springer, 2005 17(2021), 3 vom: 01. Apr., Seite 319-331 (DE-627)493205713 (DE-600)2195084-2 1614-5054 nnns volume:17 year:2021 number:3 day:01 month:04 pages:319-331 https://dx.doi.org/10.1007/s11334-021-00391-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 ASE AR 17 2021 3 01 04 319-331
spelling	10.1007/s11334-021-00391-w doi (DE-627)SPR044715323 (SPR)s11334-021-00391-w-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl Das, Kamal verfasserin aut Parallel simulation of cyber-physical systems 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract Model-based design (MBD) in systems engineering is a well-accepted technique to abstract, analyze, verify, and validate complex systems. In MBD, we design a mathematical model of the system to virtually execute and test systems via model simulations to understand the system dynamics better. Computing model simulations has their challenges; one is to ensure that the simulation trajectory preserves the model semantics. Besides, computing many simulation trajectories over a long time-horizon must be time efficient for rapid respond to system engineers. In this work, we address these challenges in simulating models of cyber-physical systems (CPS), particularly systems possessing mixed discrete–continuous dynamics. We focus on the subclass of CPS’s hybrid automata models, where Jump predicates are restricted to polygonal constraints and present a numerical simulation engine that can efficiently compute many random simulations in parallel by exploiting the parallel computing capability in modern multicore processors. Our simulation engine implements a lock-free parallel breadth-first-search (BFS)-like algorithm and is implemented in the model-checking tool XSpeed. In addition, an application of our simulation engine in property verification of CPS models has been illustrated on two benchmarks. Some model coverage metrics have been defined that users of the tool can specify to set the desired thoroughness of testing with simulations. We demonstrate the performance gains of our simulation engine over SpaceEx and CORA, the modern model checkers and simulators for affine hybrid systems. Hybrid systems (dpeaa)DE-He213 Hybrid automaton (dpeaa)DE-He213 Simulation trajectory (dpeaa)DE-He213 Parallel simulations (dpeaa)DE-He213 Gurung, Amit verfasserin aut Ray, Rajarshi verfasserin aut Enthalten in Innovations in systems and software engineering London : Springer, 2005 17(2021), 3 vom: 01. Apr., Seite 319-331 (DE-627)493205713 (DE-600)2195084-2 1614-5054 nnns volume:17 year:2021 number:3 day:01 month:04 pages:319-331 https://dx.doi.org/10.1007/s11334-021-00391-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 ASE AR 17 2021 3 01 04 319-331
allfields_unstemmed	10.1007/s11334-021-00391-w doi (DE-627)SPR044715323 (SPR)s11334-021-00391-w-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl Das, Kamal verfasserin aut Parallel simulation of cyber-physical systems 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract Model-based design (MBD) in systems engineering is a well-accepted technique to abstract, analyze, verify, and validate complex systems. In MBD, we design a mathematical model of the system to virtually execute and test systems via model simulations to understand the system dynamics better. Computing model simulations has their challenges; one is to ensure that the simulation trajectory preserves the model semantics. Besides, computing many simulation trajectories over a long time-horizon must be time efficient for rapid respond to system engineers. In this work, we address these challenges in simulating models of cyber-physical systems (CPS), particularly systems possessing mixed discrete–continuous dynamics. We focus on the subclass of CPS’s hybrid automata models, where Jump predicates are restricted to polygonal constraints and present a numerical simulation engine that can efficiently compute many random simulations in parallel by exploiting the parallel computing capability in modern multicore processors. Our simulation engine implements a lock-free parallel breadth-first-search (BFS)-like algorithm and is implemented in the model-checking tool XSpeed. In addition, an application of our simulation engine in property verification of CPS models has been illustrated on two benchmarks. Some model coverage metrics have been defined that users of the tool can specify to set the desired thoroughness of testing with simulations. We demonstrate the performance gains of our simulation engine over SpaceEx and CORA, the modern model checkers and simulators for affine hybrid systems. Hybrid systems (dpeaa)DE-He213 Hybrid automaton (dpeaa)DE-He213 Simulation trajectory (dpeaa)DE-He213 Parallel simulations (dpeaa)DE-He213 Gurung, Amit verfasserin aut Ray, Rajarshi verfasserin aut Enthalten in Innovations in systems and software engineering London : Springer, 2005 17(2021), 3 vom: 01. Apr., Seite 319-331 (DE-627)493205713 (DE-600)2195084-2 1614-5054 nnns volume:17 year:2021 number:3 day:01 month:04 pages:319-331 https://dx.doi.org/10.1007/s11334-021-00391-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 ASE AR 17 2021 3 01 04 319-331
allfieldsGer	10.1007/s11334-021-00391-w doi (DE-627)SPR044715323 (SPR)s11334-021-00391-w-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl Das, Kamal verfasserin aut Parallel simulation of cyber-physical systems 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract Model-based design (MBD) in systems engineering is a well-accepted technique to abstract, analyze, verify, and validate complex systems. In MBD, we design a mathematical model of the system to virtually execute and test systems via model simulations to understand the system dynamics better. Computing model simulations has their challenges; one is to ensure that the simulation trajectory preserves the model semantics. Besides, computing many simulation trajectories over a long time-horizon must be time efficient for rapid respond to system engineers. In this work, we address these challenges in simulating models of cyber-physical systems (CPS), particularly systems possessing mixed discrete–continuous dynamics. We focus on the subclass of CPS’s hybrid automata models, where Jump predicates are restricted to polygonal constraints and present a numerical simulation engine that can efficiently compute many random simulations in parallel by exploiting the parallel computing capability in modern multicore processors. Our simulation engine implements a lock-free parallel breadth-first-search (BFS)-like algorithm and is implemented in the model-checking tool XSpeed. In addition, an application of our simulation engine in property verification of CPS models has been illustrated on two benchmarks. Some model coverage metrics have been defined that users of the tool can specify to set the desired thoroughness of testing with simulations. We demonstrate the performance gains of our simulation engine over SpaceEx and CORA, the modern model checkers and simulators for affine hybrid systems. Hybrid systems (dpeaa)DE-He213 Hybrid automaton (dpeaa)DE-He213 Simulation trajectory (dpeaa)DE-He213 Parallel simulations (dpeaa)DE-He213 Gurung, Amit verfasserin aut Ray, Rajarshi verfasserin aut Enthalten in Innovations in systems and software engineering London : Springer, 2005 17(2021), 3 vom: 01. Apr., Seite 319-331 (DE-627)493205713 (DE-600)2195084-2 1614-5054 nnns volume:17 year:2021 number:3 day:01 month:04 pages:319-331 https://dx.doi.org/10.1007/s11334-021-00391-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 ASE AR 17 2021 3 01 04 319-331
allfieldsSound	10.1007/s11334-021-00391-w doi (DE-627)SPR044715323 (SPR)s11334-021-00391-w-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl Das, Kamal verfasserin aut Parallel simulation of cyber-physical systems 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract Model-based design (MBD) in systems engineering is a well-accepted technique to abstract, analyze, verify, and validate complex systems. In MBD, we design a mathematical model of the system to virtually execute and test systems via model simulations to understand the system dynamics better. Computing model simulations has their challenges; one is to ensure that the simulation trajectory preserves the model semantics. Besides, computing many simulation trajectories over a long time-horizon must be time efficient for rapid respond to system engineers. In this work, we address these challenges in simulating models of cyber-physical systems (CPS), particularly systems possessing mixed discrete–continuous dynamics. We focus on the subclass of CPS’s hybrid automata models, where Jump predicates are restricted to polygonal constraints and present a numerical simulation engine that can efficiently compute many random simulations in parallel by exploiting the parallel computing capability in modern multicore processors. Our simulation engine implements a lock-free parallel breadth-first-search (BFS)-like algorithm and is implemented in the model-checking tool XSpeed. In addition, an application of our simulation engine in property verification of CPS models has been illustrated on two benchmarks. Some model coverage metrics have been defined that users of the tool can specify to set the desired thoroughness of testing with simulations. We demonstrate the performance gains of our simulation engine over SpaceEx and CORA, the modern model checkers and simulators for affine hybrid systems. Hybrid systems (dpeaa)DE-He213 Hybrid automaton (dpeaa)DE-He213 Simulation trajectory (dpeaa)DE-He213 Parallel simulations (dpeaa)DE-He213 Gurung, Amit verfasserin aut Ray, Rajarshi verfasserin aut Enthalten in Innovations in systems and software engineering London : Springer, 2005 17(2021), 3 vom: 01. Apr., Seite 319-331 (DE-627)493205713 (DE-600)2195084-2 1614-5054 nnns volume:17 year:2021 number:3 day:01 month:04 pages:319-331 https://dx.doi.org/10.1007/s11334-021-00391-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 ASE AR 17 2021 3 01 04 319-331
language	English
source	Enthalten in Innovations in systems and software engineering 17(2021), 3 vom: 01. Apr., Seite 319-331 volume:17 year:2021 number:3 day:01 month:04 pages:319-331
sourceStr	Enthalten in Innovations in systems and software engineering 17(2021), 3 vom: 01. Apr., Seite 319-331 volume:17 year:2021 number:3 day:01 month:04 pages:319-331
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Hybrid systems Hybrid automaton Simulation trajectory Parallel simulations
dewey-raw	004
isfreeaccess_bool	false
container_title	Innovations in systems and software engineering
authorswithroles_txt_mv	Das, Kamal @@aut@@ Gurung, Amit @@aut@@ Ray, Rajarshi @@aut@@
publishDateDaySort_date	2021-04-01T00:00:00Z
hierarchy_top_id	493205713
dewey-sort	14
id	SPR044715323
language_de	englisch
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author	Das, Kamal
spellingShingle	Das, Kamal ddc 004 bkl 54.50 misc Hybrid systems misc Hybrid automaton misc Simulation trajectory misc Parallel simulations Parallel simulation of cyber-physical systems
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topic	ddc 004 bkl 54.50 misc Hybrid systems misc Hybrid automaton misc Simulation trajectory misc Parallel simulations
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title	Parallel simulation of cyber-physical systems
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title_sort	parallel simulation of cyber-physical systems
title_auth	Parallel simulation of cyber-physical systems
abstract	Abstract Model-based design (MBD) in systems engineering is a well-accepted technique to abstract, analyze, verify, and validate complex systems. In MBD, we design a mathematical model of the system to virtually execute and test systems via model simulations to understand the system dynamics better. Computing model simulations has their challenges; one is to ensure that the simulation trajectory preserves the model semantics. Besides, computing many simulation trajectories over a long time-horizon must be time efficient for rapid respond to system engineers. In this work, we address these challenges in simulating models of cyber-physical systems (CPS), particularly systems possessing mixed discrete–continuous dynamics. We focus on the subclass of CPS’s hybrid automata models, where Jump predicates are restricted to polygonal constraints and present a numerical simulation engine that can efficiently compute many random simulations in parallel by exploiting the parallel computing capability in modern multicore processors. Our simulation engine implements a lock-free parallel breadth-first-search (BFS)-like algorithm and is implemented in the model-checking tool XSpeed. In addition, an application of our simulation engine in property verification of CPS models has been illustrated on two benchmarks. Some model coverage metrics have been defined that users of the tool can specify to set the desired thoroughness of testing with simulations. We demonstrate the performance gains of our simulation engine over SpaceEx and CORA, the modern model checkers and simulators for affine hybrid systems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021
abstractGer	Abstract Model-based design (MBD) in systems engineering is a well-accepted technique to abstract, analyze, verify, and validate complex systems. In MBD, we design a mathematical model of the system to virtually execute and test systems via model simulations to understand the system dynamics better. Computing model simulations has their challenges; one is to ensure that the simulation trajectory preserves the model semantics. Besides, computing many simulation trajectories over a long time-horizon must be time efficient for rapid respond to system engineers. In this work, we address these challenges in simulating models of cyber-physical systems (CPS), particularly systems possessing mixed discrete–continuous dynamics. We focus on the subclass of CPS’s hybrid automata models, where Jump predicates are restricted to polygonal constraints and present a numerical simulation engine that can efficiently compute many random simulations in parallel by exploiting the parallel computing capability in modern multicore processors. Our simulation engine implements a lock-free parallel breadth-first-search (BFS)-like algorithm and is implemented in the model-checking tool XSpeed. In addition, an application of our simulation engine in property verification of CPS models has been illustrated on two benchmarks. Some model coverage metrics have been defined that users of the tool can specify to set the desired thoroughness of testing with simulations. We demonstrate the performance gains of our simulation engine over SpaceEx and CORA, the modern model checkers and simulators for affine hybrid systems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021
abstract_unstemmed	Abstract Model-based design (MBD) in systems engineering is a well-accepted technique to abstract, analyze, verify, and validate complex systems. In MBD, we design a mathematical model of the system to virtually execute and test systems via model simulations to understand the system dynamics better. Computing model simulations has their challenges; one is to ensure that the simulation trajectory preserves the model semantics. Besides, computing many simulation trajectories over a long time-horizon must be time efficient for rapid respond to system engineers. In this work, we address these challenges in simulating models of cyber-physical systems (CPS), particularly systems possessing mixed discrete–continuous dynamics. We focus on the subclass of CPS’s hybrid automata models, where Jump predicates are restricted to polygonal constraints and present a numerical simulation engine that can efficiently compute many random simulations in parallel by exploiting the parallel computing capability in modern multicore processors. Our simulation engine implements a lock-free parallel breadth-first-search (BFS)-like algorithm and is implemented in the model-checking tool XSpeed. In addition, an application of our simulation engine in property verification of CPS models has been illustrated on two benchmarks. Some model coverage metrics have been defined that users of the tool can specify to set the desired thoroughness of testing with simulations. We demonstrate the performance gains of our simulation engine over SpaceEx and CORA, the modern model checkers and simulators for affine hybrid systems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021
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title_short	Parallel simulation of cyber-physical systems
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Parallel simulation of cyber-physical systems

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