ChEESE introduces workflow management system prototype for geoscience applications
27 April 2020

Many scientific applications, in particular in the geoscience domain, are built by interconnecting several functionally-independent components within a common data- and workflow scenario. Such applications have to deal with the issues of the coordinated execution of the components as well as organization of the data exchange between them (see Figure 1). Those issues of workflow-based execution are even worse considering that the components used in ChEESE have external data dependencies and are deployed on a distributed, heterogeneous infrastructure.



Figure 1. Simple workflow deployment on distributed infrastructure with external data dependencies.

For the automation of the applications execution with distributed, far-reaching paths between the data-interconnected software components, special workflow management Systems (WMS) are necessary. WMS are simple in terms of usage but at the same time powerful in terms of features functionality for dealing with the issues of management complex workflow-based applications. The wide-spread WMS either rely on an excessive middleware stack requiring special infrastructure permissions (such as in the case of Pegasus, Copernicus, etc.) or are too problem-oriented (such as in Makeflow, Parsl, FireWorks, etc.) which makes their integration with production infrastructures (like HPC) and use for a general applications design very complicated. Therefore, an effort was made in the ChEESE project to develop a complementary WMS that would allow the workflow design and development with the minimum of implementation efforts.

The WMS system developed in ChEESE, called WMS-light (see Figure 2), strives to provide a flexible framework of light-weight and portable software tools and services that can simplify the development and execution of workflow-based applications while possibly introducing the lowest overhead to the infrastructure resources' system software stack. This approach is achieved by leveraging a high level of decentralization of the major workflow management components, their service-orientation and a unified data management platform.

The WMS-light main features are:

  • intuitive design of workflow scenarios by the users via its workflow specification API.
  • backtracking of properties of all past executions provided by WMS-light’s persistent data layer.
  • basic analytics functionalities by means of a rich querying system.
  • easy adaptability of WMS-light applications by reusing components of already existing WMS-light applications.
  • automatic data placement for application components that serves locality of the input and output data. This allows to transform existing scripts into WMS-light workflows with a minimally invasive effort and easy coupling of already existing workflow components.
  • basic executable unit of WMS-light can be any binary application or script 
  • extendibility of application components with additional monitoring metrics (along with the automatically gathered hardware data) by a API1.
  • open architecture and modular design for straightforward extension of the basic WMS functionality or even integration with the other WMS.

Figure 2. Architecture of ChEESE WMS-light workflow management system.

The prototype release is fully open source and can be found for download at the link:

For questions about the usage of WMS-light system please contact Christoph Niethammer ( or Alexey Cheptsov ( 

ChEESE presents at High Performance Innovation Conference
13 April 2020
High Performance Innovation Conference - ChEESE


ChEESE coordinator Arnau Folch presented a talk titled “Predicting techniques for global hazards, earthquakes and volcanoes: ChEESE” at the High Performance Innovation Conference, held online on March 30, 2020. 

In his talk, Folch introduced the objectives of ChEESE and then focused on the project´s flagship codes and pilot demonstrators

The event, organized by the Spanish Supercomputing Network and the Barcelona Supercomputing Center, aimed to promote the adoption of High Performance Computing (HPC) among the academic, business, and public administration sectors.

The European Centers of Excellence (CoE) took turns introducing their work in developing leading edge technologies to promote the adoption of advanced HPC in industry and public administration, and increasing competitiveness for European companies and SMEs through access to CoE expertise and services.

These CoEs cover important areas like renewable energy, materials modelling and design, molecular and atomic modelling, climate change, global system science, and bio-molecular research, and tools to improve HPC applications performance.

Watch all the talks:

Competence Centres in HPC. Bastian Koller, HLRS, Germany.
BioExcel: Centre of Excellence for Computational Biomolecular Research, Josep Ll. Gelpí.
COMPBIOMED: Centre of Excellence in Computational Biomedicine. Mariano Vázquez.
ChEESE: Center of Excellence In Solid Earth. Arnau Folch.
EoCoE: Energy Oriented Centre of Excellence. Herbert Owen.
ESiWACE: Centre of Excellence in Simulation of Weather and Climate in Europe. Kim Serradell.
MAX: MAterials design at the eXascale. Stephan Mohr.
POP: Performance Optimisation and Productivity Centre of Excellence in HPC, Jesús Labarta.
EXCELLERAT: European Centre of Excellence for Engineering Applications. Daniel Mira.