Spike - a command line tool for continuous, stochastic ... · [email protected] AWPN October...
Transcript of Spike - a command line tool for continuous, stochastic ... · [email protected] AWPN October...
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[email protected] AWPN October 2018 01
Spike - a command line tool forcontinuous, stochastic & hybrid simulation
of (coloured) Petri nets
Jacek Chodak, Monika Heiner (supervisor)Computer Science Institute, Brandenburg University of Technology
Cottbus, [email protected], [email protected]
http://www-dssz.informatik.tu-cottbus.de/DSSZ/Software/Spike
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Agenda
* PetriNuts framework* Why Spike* Spike functionality - Simulation - Conversion - Reduction - Reproducibility* Architecture* Use cases* Present status* Future works
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PetriNuts Framework
CHARLIE [1] • Structural analysis • Analyses building on incidence matrix • Reachability graph analysis • Siphon/trap computation • Explicite CTL model checking • Structural reduction
SNOOPY [3, 4]• Modelling• Simulation: - stochastic, - continuous, - hybrid• Animation
MARCIE [2]• Symbolic analysis (PN) - state space analysis - CTL model checking• Symbolic analysis (SPN) - standard numerical analysis, - CSRL model checking• Simulative analysis (SPN) - standard numerical analysis - PLTc model checking • explicit approximative numerical analysis
SPIKE• Distributed simulation: - stochastic, - continuous, - hybrid
PATTY [5]• Web based animation
References:• [1] M Heiner, M Schwarick and J Wegener: PETRI NETS 2015• [2] M Heiner, C Rohr and M Schwarick: PETRI NETS 2013• [3] M Herajy, F Liu, C Rohr and M Heiner: BMC Systems Biology 2017• [4] C Rohr, W Marvan, M Heiner: Bioinformatics 2010• [5] K Schulz, BA thesis, BTU Cottbus, CS department 2008
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[email protected] AWPN October 2018 04
Why Spike
* Reproducibility
* Simplifying workflow of simulation experiments
* Speedup simulation Models can contain dozens of thousands of nodes. To speed up simulation, a model can be
reduced or divided into modules (spatial decomposition, decomposition by node types) and simulated in a distributed way.
There are many parameters: model parameters (e.g., initial marking, kinetic constants); simulations
parameters(e.g., type of algm, length of trace, number of stochastic runs). Experiments with Spike are documented by configuration files.
CLI controlling multiple simulations without user intervention; typically for different model configurations and/or simulator configurations.
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[email protected] AWPN October 2018 04
Why Spike
* Reproducibility
* Simplifying workflow of simulation experiments
* Speedup simulation Models can contain dozens of thousands of nodes. To speed up simulation, a model can be
reduced or divided into modules (spatial decomposition, decomposition by node types) and simulated in a distributed way.
There are many parameters: model parameters (e.g., initial marking, kinetic constants); simulations
parameters(e.g., type of algm, length of trace, number of stochastic runs). Experiments with Spike are documented by configuration files.
CLI controlling multiple simulations without user intervention; typically for different model configurations and/or simulator configurations.
![Page 6: Spike - a command line tool for continuous, stochastic ... · jacek.chodak@b-tu.de AWPN October 2018 01 Spike - a command line tool for continuous, stochastic & hybrid simulation](https://reader036.fdocuments.us/reader036/viewer/2022070813/5f0ce7c97e708231d437b674/html5/thumbnails/6.jpg)
[email protected] AWPN October 2018 04
Why Spike
* Reproducibility
* Simplifying workflow of simulation experiments
* Speedup simulation Models can contain dozens of thousands of nodes. To speed up simulation, a model can be
reduced or divided into modules (spatial decomposition, decomposition by node types) and simulated in a distributed way.
There are many parameters: model parameters (e.g., initial marking, kinetic constants); simulations
parameters(e.g., type of algm, length of trace, number of stochastic runs). Experiments with Spike are documented by configuration files.
CLI controlling multiple simulations without user intervention; typically for different model configurations and/or simulator configurations.
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Simulation
* CPN - Continuous Petri Net (ODEs)* HPN - Hybrid Petri Net
SPN CPN
HPN
<=>
<=>
<=>
Supported (coloured) PN classes:* SPN - Stochastic Petri Net (CONTINUOUS TIME MARKOV CHAINS)
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Simulation
* SPN - Stochastic Petri Net (CONTINUOUS TIME MARKOV CHAINS)* CPN - Continuous Petri Net (ODEs)* HPN - Hybrid Petri Net
SPN CPN
HPN
<=>
<=>
<=>
Supported (coloured) PN classes:
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Simulation
* Stochastic
* Continuous
* Hybrid
time
time
time
CPN, HPN => SPN
SPN, HPN => CPN
SPN, CPN => HPN
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Simulation
* Stochastic
* Continuous
* Hybrid
time
time
time
CPN, HPN => SPN
SPN, HPN => CPN
SPN, CPN => HPN
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Conversion
ANDL
CANDL
SBML
PNML
ERODE
human readable formats for Petri nets and Coloured Petri nets, respectively, used internally by the PetriNuts framework
(the Systems Biology Markup Language) - an XML-based representation format designed to exchange computational models of biological processes
a tool for the evaluation and reduction of chemical reactionnetworks readed as ordinary differential equations (ODEs)
an XML-based interchange format for Petri nets (no time)
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Conversion
ANDL
CANDLSBML
PNML
ERODE
SPIKE ANDL
ERODE
Import Export
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ReductionModel
Reduced Model
Reduction* Clean siphons* Constant places* ERODE Further reductions may be
applied by converting a model to the ERODE format, if the model is to be read as ordinary differential equations (ODEs).
* further general reduction rules under consideration
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[email protected] AWPN October 2018 09
ReductionModel
Reduced Model
Reduction* Clean siphons* Constant places* ERODE Further reductions may be
applied by converting a model to the ERODE format, if the model is to be read as ordinary differential equations (ODEs).
* further general reduction rules under consideration
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Reproducibilityconfiguration: { model: { constants: { ... } places: { ... } observers: { ... } }
* model configuration** constants
** observers** initial states(arc weights, initial markings, kinetic constants)
* simulation configurations** exports
simulation : { ... export: { ... } } ...}
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Reproducibilityconfiguration: { model: { constants: { ... } places: { ... } observers: { ... } }
* model configuration** constants
** observers** initial states(arc weights, initial markings, kinetic constants)
* simulation configurations** exports
simulation : { ... export: { ... } } ...}
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Reproducibility
* Definition of constantsconstants: { // name of a group all: { /∗ if constant does not exist ∗ then it will be created and ∗ can be used in the configuration , ∗ for example in defining a place marking ∗/ M: ”D/2 + 1” }}
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Reproducibility
places: {// example of use of the newly created constant MP: "1000`(M,M)"P_2_2: 500
}
* Set marking for places
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Reproducibility
observers: {place: {OP01: {function: "P_1_1 + P_2_3"
}}
}
* Definition of auxiliary variables (observers) which allow for extra measures by defining numerical functions; depending on the type of observer, it can be defined for places, transitions or simultaneously for places, transitions and constants
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Reproducibility
simulation: { name: "Diffusion" // Name of a simulation type: stochastic // [continuous, stochastic, hybrid] solver: direct // [BDF, classic, direct, delta-leaping, static, staticAcc] threads: 0 // 0 - try to get the number of concurrent threads supported by the hardware interval: 1:1:1000// start : step : end (startTimePoint : intervalSize : endTimePoint) runs: 10 // Applicable only for stochastic sim // continues conf iniStep: 0.1 linSolver: CVDense //[CVDense, CVSpgmr, CVDiag, CVSpbcg, and CVSptfqmr] relTol: 1.0e-5 absTol: 1.0e-10 // Export export: { places: [] // Array of places to save (if empty export all) transitions: [] to: "testCase-diffusion-stochastic.csv" }}
* Defining multiple simulation configurations, which permits to run multiple experiments for one model configuration
//simulation: { ...}
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Reproducibility
export: {// Array of places to save,// including colored places like P// in this example (if empty, export all)places: ["P_1_1", "OP01", "Grid.*", "D", "P"]// Array of transitions to save,// including colored transitionstransitions: ["t3_1_1_1_2", "t3", "t3" ]// Array of observers to save (if empty, export all)observers: ["M01", "OT01"]to: "sim01-file01.csv"
}export: {...}
* Defining multiple exports of simulation results by use of regular expressions over the nodes; any combination of places, transitions and observers, coloured and uncoloured, in one file
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Architecture
* Modularity Module | Command | Description------------------------------------------------------------------------- Main | version | display version of Spike CLI | help | display help for a given command Configuration | exe | execute configuration script Converter | load | load a model from a given file | save | save a model to a given file | prune | prune a model | eval | evaluate constants and places | unfold | unfold a coloured model Simulation | sim | run a simulation of the model
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Architecture
* Command queue, a typical use case
Command queue
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Architecture
* Command queue, a typical use case
CLICommand queueexe
user cmd: spike exe -f confFile
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Architecture
* Command queue, a typical use case
CLI exeCommand queueexe
user cmd: spike exe -f confFile
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Architecture
* Command queue, a typical use case
CLI exeCommand queue
Configurationexe exe-
user cmd: spike exe -f confFile
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Architecture
* Command queue, a typical use case
CLI exeload
Command queueConfiguration
exe exeload-
user cmd: spike exe -f confFile
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Architecture
* Command queue, a typical use case
CLI exeload
unfold
Command queueConfiguration
exe exeunfold-
user cmd: spike exe -f confFile
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Architecture
* Command queue, a typical use case
CLI exeload
unfoldeval
Command queueConfiguration
exe exeeval-
user cmd: spike exe -f confFile
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Architecture
* Command queue, a typical use case
CLI exeload
unfoldevalsim
Command queueConfiguration
exe exesim-
user cmd: spike exe -f confFile
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[email protected] AWPN October 2018 17
Architecture
* Command queue, a typical use case
CLI exeload
unfoldevalsim
Command queueConfigurationload, unfold, eval, sim
Converter
exe exe
load
--
user cmd: spike exe -f confFile
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[email protected] AWPN October 2018 17
Architecture
* Command queue, a typical use case
CLI exeload
unfoldevalsim
Command queueConfigurationload, unfold, eval, sim
Converter
exe exe
unfold---
user cmd: spike exe -f confFile
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[email protected] AWPN October 2018 17
Architecture
* Command queue, a typical use case
CLI exeload
unfoldevalsim
Command queueConfigurationload, unfold, eval, sim
Converter
exe exe
eval----
user cmd: spike exe -f confFile
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[email protected] AWPN October 2018 17
Architecture
* Command queue, a typical use case
CLI exeload
unfoldevalsim
Command queueConfigurationload, unfold, eval, sim
ConverterSimulation
load, unfold, eval
exe exe
sim
-----
user cmd: spike exe -f confFile
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Use case
1 Load model2 Set model configuration3 Determine set of simulation configurations4 For( each simulation configuration ) {5 Run simulation6 Save results of the simulation7 }
Compare how a model behaves under different types of simulation algorithms or under different configurations of a given simulation algorithm.
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[email protected] AWPN October 2018 19
Present status
Mode
l a
nd simulation configuration
Model
Reduced Model
Deco
mpos
ition
Reduction
Components
Finding
interface
nodes
Distribution
Distributed model
synchronization
Simulation
Co
mponen
ts with interfaces
Traces
Reproduciblesimulation
Export
Redu
ction
TraceAnalysis
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[email protected] AWPN October 2018 19
Present status
Mode
l a
nd simulation configuration
Model
Reduced Model
Deco
mpos
ition
Reduction
Components
Finding
interface
nodes
Distribution
Distributed model
synchronization
Simulation
Co
mponen
ts with interfaces
Traces
Reproduciblesimulation
Export
Redu
ction
TraceAnalysis
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[email protected] AWPN October 2018 19
Present status
Mode
l a
nd simulation configuration
Model
Reduced Model
Deco
mpos
ition
Reduction
Components
Finding
interface
nodes
Distribution
Distributed model
synchronization
Simulation
Co
mponen
ts with interfaces
Traces
Reproduciblesimulation
Export
Redu
ction
TraceAnalysis
![Page 40: Spike - a command line tool for continuous, stochastic ... · jacek.chodak@b-tu.de AWPN October 2018 01 Spike - a command line tool for continuous, stochastic & hybrid simulation](https://reader036.fdocuments.us/reader036/viewer/2022070813/5f0ce7c97e708231d437b674/html5/thumbnails/40.jpg)
[email protected] AWPN October 2018 19
Present status
Mode
l a
nd simulation configuration
Model
Reduced Model
Deco
mpos
ition
Reduction
Components
Finding
interface
nodes
Distribution
Distributed model
synchronization
Simulation
Co
mponen
ts with interfaces
Traces
Reproduciblesimulation
Export
Redu
ction
TraceAnalysis
![Page 41: Spike - a command line tool for continuous, stochastic ... · jacek.chodak@b-tu.de AWPN October 2018 01 Spike - a command line tool for continuous, stochastic & hybrid simulation](https://reader036.fdocuments.us/reader036/viewer/2022070813/5f0ce7c97e708231d437b674/html5/thumbnails/41.jpg)
[email protected] AWPN October 2018 20
Future works
Mode
l a
nd simulation configuration
Model
Reduced Model
Deco
mpos
ition
Reduction
Components
Finding
interface
nodes
Distribution
Distributed model
synchronization
Simulation
Co
mponen
ts with interfaces
Traces
Reproduciblesimulation
Export
Redu
ction
TraceAnalysis
X
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[email protected] AWPN October 2018 20
Future works
Mode
l a
nd simulation configuration
Model
Reduced Model
Deco
mpos
ition
Reduction
Components
Finding
interface
nodes
Distribution
Distributed model
synchronization
Simulation
Co
mponen
ts with interfaces
Traces
Reproduciblesimulation
Export
Redu
ction
TraceAnalysis
X
X
X
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Future works
Mode
l a
nd simulation configuration
Model
Reduced Model
Deco
mpos
ition
Reduction
Components
Finding
interface
nodes
Distribution
Distributed model
synchronization
Simulation
Co
mponen
ts with interfaces
Traces
Reproduciblesimulation
Export
Redu
ction
TraceAnalysis
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Thank you for your attention
http://www-dssz.informatik.tu-cottbus.de/DSSZ/Software/Spike
Any questions are welcome