Mathieu Leclaire // Romain Reuillon // Mark Hammons
ISCPIF // BioEmergences // FBI
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| Large sets of raw data | Large sets of parameters |
These processings are time consuming




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European Grid Infrastructure |
| It exposes a workflow formalism for describing an experiment | ![]() |
| It transparently delegates computational loads to massively parallel environments | ![]() |
Prototype small
Experiment large
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A Task runs an executable, |
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receives and produces Variables. |
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Variables navigate from one Task to another by means of Transitions |
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eventually doing Loops. |
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| Design you experiment in OpenMOLE | At runtime |
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| Data processing | Optimization | Sensitivity analysis | Design of experiments | Calibration |
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The same Task can be run localy |
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or by taking leverage of the local multicores |
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or on a Cluster (PBS for instance) |
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or on the Grid. |
| ... |
| Requires | Produces |
| - imageJ2 macro | - processed images |
| - set of images | - other results |
| - set of parameters | |
| - and optionaly imageJ2 plugins |
val i1 = Prototype[Int]("i1")
val i2 = Prototype[Int]("i2")
val j = Prototype[Int]("j")
val hello = GroovyTask("hello", "j = Model.compute(i1, i2)")
hello addInput i1
hello addInput i2
hello addOutput j
hello addLib "/path/to/model.jar"
val exploration = ExplorationTask(
"exploration",
Factor(i1, 0 to 100 by 2 toDomain) x
Factor(i2, new UniformIntDistribution take 10)
)
val ex = exploration -< (hello by 10 on biomed) toExecution
ex.start
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@OpenMOLE |
The Bioemergences project |
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C Image processing portal access daily productions 10000 jobs / day |
