Is the grid driving you crazy? Relax! Openmole makes it easy!

Mathieu Leclaire // Romain Reuillon

ISCPIF // GeodiverCity ERC

Naturally parallel algorithms
permit to
leverage parallelism

Methods

Design of experiments	Optimization	Data processing

These methods are time consuming

Computing power

• Personal computer // 1 to 8 cores
• Computing server // up to 50 cores
• Cluster // up to 200 cores
• Grid // > 2000 cores

What does OpenMOLE do ?

It implements exploration algorithms
It transparently delegates computational loads to massively parallel environments

Upscaling

Prototype small
Experiment large

Embed your model as a black box

C
R
C++
Java
Scala
Scilab
Octave
Python
Netlogo
...

Environments already supported by OpenMOLE

• multi threading
• remote SSH server
• PBS Cluster
• EGI
• Dirac
• soon : SLURM, SGE, other cluster batch systems
• ... and later : private and academic clouds

Powered by GridScale

OpenMOLE from the GUI

OpenMOLE from DSL

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

OpenMOLE from the web server