Workflow environments for Scientific Applications

The unstoppable proliferation of novel computing and sensing device technologies, and the ever-growing demand for data-intensive applications in the edge and cloud, are driving a paradigm shift in computing around the dynamic, intelligent and yet seamless interconnection of IoT, edge and cloud resources in one single computing system to form a continuum. Many research...

With present computational capabilities and data volumes entering the Exascale Era, digital twins of the Earth system will be able to mimic the different system components (atmosphere, ocean, land, lithosphere) with unrivalled precision, providing analyses, forecasts, and what-if scenarios for natural hazards and resources from their genesis phases and across their temporal...

For Artificial Intelligence (AI) to become fully pervasive it needs resources at the edge of the network. The cloud can provide the processing power needed for big data, but edge computing is located close to where data are produced and is therefore crucial to their timely, flexible, and secure management.

AI-SPRINT will define a framework for...

Today developers lack tools that enable the development of complex workflows involving HPC simulation and modelling with data analytics (DA) and machine learning (ML). The eFlows4HPC project aims to deliver a workflow software stack and an additional set of services to enable the integration of HPC simulation and modelling with big data analytics and machinelearning in...