Suchergebnisse

Presentation of EOSC-Nordic & NICEST2 projects at the INES (Infrastructure for Norwegian Earth System modelling) annual meeting, September 2020. Presentation given online. The goal was to coordinate with national initiative (INES) for Climate modelling. ; EOSC-Nordic project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 857652

Informal presentation given online on 22/02/2021, 10:00 CET - 11:00 CET to discuss and explain the cross-border computing needs from the EOSC-Nordic Climate use case and in particular potential pilot access to LUMI for running containerized Earth System Models & workflows. Context, short and longer-term objectives Requirements for running containerized Earth System Models Timeline and pilot access ; The EOSC-Nordic project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 857652. This activity was also supported by the Nordic e-Infrastructure Collaboration (NeIC), which is part of NordForsk, in the framework of the NICEST2 project.

This deliverable aims to investigate relevant EOSC and national services that can be leveraged to support cross-border data management, data-driven distributed computing and research workflows for two research communities (climate and natural language processing). Emphasis is placed on data aggregation and interlinking of sharing and active data storage. ; The EOSC-Nordic project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 857652. This deliverable is EC submitted, not approved.

Scientific workflows are a cornerstone of modern scientific computing, and they have underpinned some of the most significant discoveries of the last decade. Many of these workflows have high computational, storage, and/or communication demands, and thus must execute on a wide range of large-scale platforms, from large clouds to upcoming exascale HPC platforms. Workflows will play a crucial role in the data-oriented and post-Moore's computing landscape as they democratize the application of cutting-edge research techniques, computationally intensive methods, and use of new computing platforms. As workflows continue to be adopted by scientific projects and user communities, they are becoming more complex. Workflows are increasingly composed of tasks that perform computations such as short machine learning inference, multi-node simulations, long-running machine learning model training, amongst others, and thus increasingly rely on heterogeneous architectures that include CPUs but also GPUs and accelerators. The workflow management system (WMS) technology landscape is currently segmented and presents significant barriers to entry due to the hundreds of seemingly comparable, yet incompatible, systems that exist. Another fundamental problem is that there are conflicting theoretical bases and abstractions for a WMS. Systems that use the same underlying abstractions can likely be translated between, which is not the case for systems that use different abstractions. More information: https://workflowsri.org/summits/technical