Optimization of runtime and libraries, workflow deployment, resource management, and data management
• Optimize the eFlows4HPC machine learning and workflow runtimes to obtain the required levels of performance and energy efficiency.
• Enable a stable deployment platform, supporting the selected container solution for dynamic deployment of the HPC Workflow-as-a-Service concept.
• Provide a stable set of innovative storage solutions to support scientific workflows.
• Identify relevant data sources, list them in a Data Catalogue and into an Integrated Data Logistics Service.
• Implement Data Pipelines to fuel Pillars use cases. The implementations performed in the tasks of this WP will be integrated with the results of task 1.4 and into its deliverables.
This work has been supported by the eFlows4HPC project, contract #955558. This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 955558. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, Germany, France, Italy, Poland, Switzerland, Norway.