title: Reproducible Large-Scale Neuroimaging Studies with the OpenMOLE Workflow Management System.
abstract: OpenMOLE is a scientific workflow engine with a strong emphasis on workload distribution. Workflows are designed using a high level Domain Specific Language (DSL) built on top of Scala. It exposes natural parallelism constructs to easily delegate the workload resulting from a workflow to a wide range of distributed computing environments. OpenMOLE hides the complexity of designing complex experiments thanks to its DSL. Users can embed their own applications and scale their pipelines from a small prototype running on their desktop computer to a large-scale study harnessing distributed computing infrastructures, simply by changing a single line in the pipeline definition. The construction of the pipeline itself is decoupled from the execution context. The high-level DSL abstracts the underlying execution environment, contrary to classic shell-script based pipelines. These two aspects allow pipelines to be shared and studies to be replicated across different computing environments. Workflows can be run as traditional batch pipelines or coupled with OpenMOLE's advanced exploration methods in order to study the behavior of an application, or perform automatic parameter tuning. In this work, we briefly present the strong assets of OpenMOLE and detail recent improvements targeting re-executability of workflows across various Linux platforms. We have tightly coupled OpenMOLE with CARE, a standalone containerization solution that allows re-executing on a Linux host any application that has been packaged on another Linux host previously. The solution is evaluated against a Python-based pipeline involving packages such as scikit-learn as well as binary dependencies. All were packaged and re-executed successfully on various HPC environments, with identical numerical results (here prediction scores) obtained on each environment. Our results show that the pair formed by OpenMOLE and CARE is a reliable solution to generate reproducible results and re-executable pipelines. A demonstration of the flexibility of our solution showcases three neuroimaging pipelines harnessing distributed computing environments as heterogeneous as local clusters or the European Grid Infrastructure (EGI).
authors.affiliation: BioMedIA Group, Department of Computing, Imperial College London London, UK.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Passerat-Palmbach
authors.affiliation: Institut des Systemes Complexes Paris Ile de France Paris, France.
authors.forename: Romain
authors.initials: R
authors.lastname: Reuillon
authors.affiliation: Institut des Systemes Complexes Paris Ile de France Paris, France.
authors.forename: Mathieu
authors.initials: M
authors.lastname: Leclaire
authors.affiliation: BioMedIA Group, Department of Computing, Imperial College London London, UK.
authors.forename: Antonios
authors.initials: A
authors.lastname: Makropoulos
authors.affiliation: BioMedIA Group, Department of Computing, Imperial College London London, UK.
authors.forename: Emma C
authors.initials: EC
authors.lastname: Robinson
authors.affiliation: BioMedIA Group, Department of Computing, Imperial College London London, UK.
authors.forename: Sarah
authors.initials: S
authors.lastname: Parisot
authors.affiliation: BioMedIA Group, Department of Computing, Imperial College London London, UK.
authors.forename: Daniel
authors.initials: D
authors.lastname: Rueckert
keywords: high performance computing; large datasets; neuroimaging; parameter exploration; pipeline; reproducibility; workflow systems