NextSim partners, as fundamental European players in Aeronautics and Simulation, recognise that there is a need to increase the capabilities of current Computational Fluid Dynamics tools for aeronautical design by re-engineering them for extreme-scale parallel computing platforms. The backbone of NextSim is centred on the fact that, today, the capabilities of leading-edge emerging HPC architectures are not fully exploited by industrial simulation tools. Current state-of-the-art industrial solvers do not take sufficient advantage of the immense capabilities of new hardware architectures, such as streaming processors or many-core platforms. A combined research effort focusing on algorithms and HPC is the only way to make possible to develop and advance simulation tools to meet the needs of the European aeronautical industry. NextSim will focus on the development of the numerical flow solver CODA (Finite Volume and high-order discontinuous Galerkin schemes), that will be the new reference solver for aerodynamic applications inside AIRBUS group, having a significant impact in the aeronautical market. To demonstrate NextSim market impact, AIRBUS has defined a series of market relevant problems. The numerical simulation of those problems is still a challenge for the aeronautical industry and their solution, at a required accuracy and an affordable computational costs, is still not possible with the current industrial solvers. Following this idea, three additional working areas are proposed in NextSim: algorithms for numerical efficiency, algorithms for data management and the efficiency implementation of those algorithms in the most advanced HPC platforms. Finally, NextSim will provide access to project results trough the “mini-apps” concept, small pieces of software, seeking synergies with open-source components, which demonstrate the use of the novel mathematical methods and algorithms developed in CODA but that will be freely distributed to the scientific community.

The Data Infrastructure Capacities for EOSC (DICE) consortium brings together a network of computing and data centres, research infrastructures, and data repositories for the purpose to enable a European storage and data management infrastructure for EOSC, providing generic services and building blocks to store, find, access and process data in a consistent and persistent way. Specifically, DICE partners will offer 14 state-of-the-art data management services together with more than 50 PB of storage capacity. The service and resource provisioning will be accompanied by enhancing the current service offering in order to fill the gaps still present to the support of the entire research data lifecycle; solutions will be provided for increasing the quality of data and their re-usability, supporting long term preservation, managing sensitive data, and bridging between data and computing resources. All services provided via DICE will be offered through the EOSC Portal and interoperable with EOSC Core via a lean interoperability layer to allow efficient resource provisioning from the very beginning of the project. The partners will closely monitor the evolution of the EOSC interoperability framework and guidelines to comply with a) the rules of participation to onboard services into EOSC, and b) the interoperability guidelines to integrate with the EOSC Core functions. The data services offered via DICE through EOSC are designed to be agnostic to the scientific domains in order to be multidisciplinary and to fulfil the needs of different communities. The consortium aims to demonstrate their effectiveness of the service offering by integrating services with community platforms as part of the project and by engaging with new communities coming through EOSC.

CloudStars is an Open Source Research Mobility network in the field of Cloud Computing technology. The proposal combines eleven academic institutions in nine European countries (Spain, Portugal, UK, Germany, Netherlands, Italy, Austria, Poland, Switzerland), two companies in Europe (SAP in Germany, NearBy Computing in Spain), and three industrial laboratories from IBM (USA, Switzerland, and Israel). The major flow of secondments is scheduled between the academic institutions and IBM industrial labs, but also including a significant number of secondments to SAP and NearBy Computing. CloudStars will create a global reference community in open source Cloud Computing. The participants will combine theoretical skills and experience in distributed systems research with industrial open source technologies and cutting-edge Cloud and Edge infrastructures. This will increase the overall global impact of research contributions, helping to arrive to millions of interested third parties through open source communities. The general goals of the project are: 1. Increase the impact of European researchers with contributions to key open source projects and the involvement in open source communities. 2. Rise the careers of European researchers through well-established networks both across EU and with global open source players. 3. Increase the reproducibility of results in Science and Data Analytics through standard Cloud container technologies. Technical goals are: 1. Development and benchmarking of next generation container technologies leveraging open source Cloud Native Computing Foundation (CNCF) projects and GAIAX protocols. 2. Design novel cloud serverless middleware over container technologies including Function as a Service, serverless containers, and event-based orchestration. 3. Apply novel machine learning techniques for managing containerized Cloud and Edge systems, involving the infrastructure and configuration of executions and services.

Hi-EST aims to address a new class of placement problem, a challenge for computational sciences that consists in mapping workloads on top of hardware resources with the goal to maximise the performance of workloads and the utilization of resources. The objective of the placement problem is to perform a more efficient management of the computing infrastructure by continuously adjusting the number and type of resources allocated to each workload. Placement, in this context, is well known for being NP-hard, and resembles the multi-dimensional knapsack problem. Heuristics have been used in the past for different domains, providing vertical solutions that cannot be generalised. When the workload mix is heterogeneous and the infrastructure hybrid, the problem becomes even more challenging. This is the problem that Hi-EST plans to address. The approach followed will build on top of four research pillars: supervised learning of the placement properties, placement algorithms for tasks, placement algorithms for data, and software defined environments for placement enforcement. Hi-EST plans to advance research frontiers in four different areas: 1) Adaptive Learning Algorithms: by proposing the first known use of Deep Learning techniques for guiding task and data placement decisions; 2) Task Placement: by proposing the first known algorithm to map heterogeneous sets of tasks on top of systems enabled with Active Storage capabilities, and by extending unifying performance models for heterogeneous workloads to cover and unprecedented number of workload types; 3) Data Placement: by proposing the first known algorithm used to map data on top of heterogeneous sets of key/value stores connected to Active Storage technologies; and 4) Software Defined Environments (SDE): by extending SDE description languages with a still inexistent vocabulary to describe Supercomputing workloads that will be leveraged to combine data and task placement into one single decision-making process.