The European Exascale Projects combine under one roof all Exascale research related efforts funded by the European Commission (EC) in their first HPC initiative through the 7th Framework Programme. To resolve the challenges the Exascale future still holds, the various projects address all relevant research areas. Thus they span from innovative approaches to hardware design and programming models as well as to application development.
For a lookback on the last 5 years of European Exascale Projects Research Collaboration, have a look at the brochure. After these fruitful five years of collaboration, EXDCI will keep up the collaboration initiative, intensify it and streamline the development of the European HPC Ecosystem on the way to next-generation supercomputers.
"The European Exascale Projects collaboration has been a good part of the HPC Ecosystem. The EXDCI project's aim is to coordinate the collaboration between FETHPC and Centers of Excellence and the complete HPC ecosystem. I am sure that the outcome of the European Exascale projects will be influential to the future of HPC." - Dr Sergi Girona, EXDCI project coordinator.
A short description of each European Exascale project can be found here:
CRESTA brought together four of Europe’s leading supercomputing centres, with one of the world’s major equipment vendors, two of Europe’s leading programming tools providers, and six application and problem owners, to explore how to meet the exascale challenge. CRESTA focused on the use of six applications with exascale potential and used them as co-design vehicles to develop an integrated suite of technologies required to support the execution of applications at the exascale. The six co-design vehicles represented an exceptional group of applications used by European academia and industry to solve critical grand challenge issues, including: biomolecular systems, fusion energy, the virtual physiological human, numerical weather prediction and engineering.
The DEEP and DEEP-ER projects develop a novel, highly flexible Exascale-ready HPC platform based on the Cluster-Booster architecture featuring a complete, standards-based and easy-to-use software stack . A set of 11 real-world HPC applications drives a stringent co-design process with the aim of fine-tuning the architecture to actual Exascale application requirements. Both projects address key challenges on the way to next-gen supercomputers like energy efficiency, highly scalable I/O, resiliency, memory hierarchies. The research projects are funded by the European Commission and made up of 20 partners from academia and industry.
Compute efficiency and energy efficiency are more than ever major concerns for future Exascale systems.
Since October 2011, the aim of the European project called Mont-Blanc has been to design a new type of computer architecture capable of setting future global HPC standards, built from energy efficient solutions used in embedded and mobile devices. Phases 1 and 2 of the project are coordinated by the Barcelona Supercomputing Center (BSC). Phase 1 had a budget of over 14 million, including over 8 million Euros funded by the European Commission. Two years later, the European Commission granted additional 8 million Euro funds to extend the Mont-Blanc project activities until September 2016. This three year extension enabled further development of the OmpSs parallel programming model to automatically exploit multiple cluster nodes, transparent application check pointing for fault tolerance, support for ARMv8 64-bit processors, and the initial design of the Mont-Blanc Exascale architecture. The third phase of the Mont-Blanc project started in October 2015: it is coordinated by Bull, the Atos brand for technology products and software, and has a budget of 7.9 million Euros, funded by the European Commission under the Horizon 2020 programme. The third phase adopts a co-design approach to ensure that hardware and system innovations are readily translated into benefits for HPC applications. It aims at designing a new high-end HPC platform that is able to deliver a new level of performance / energy ratio when executing real applications.
EPiGRAM is an EC-funded FP7 project on exascale computing. The aim of the EPiGRAM project is to prepare Message Passing and PGAS programming models for exascale systems by fundamentally addressing their main current limitations. The concepts developed will be tested and guided by two applications in the engineering and space weather domains chosen from the suite of codes in current EC exascale projects.
The EXA2CT European project brings together experts at the cutting edge of the development of solvers, related algorithmic techniques, and HPC software architects for programming models and communication. EXA2CT will discover solver algorithms that can scale to the huge numbers of nodes at exascale; develop an exascale programming model that is usable by application developers; offer these developments to the wider community in open-source proto-applications, to enable exascale machine/software co-design and a basis for exascale applications. These proto applications will be disseminated to the reference application owners through a scientific and industrial board (SIB) to help generate momentum behind our approach.
The specific goal of NUMEXAS is the development of numerical methods for multiphysics problems in engineering based on validated models that enable scaling to millions of cores along the complete simulation pipeline: parallel pre-processing and grid generation; new numerical methods for parallel structured/unstructured multidisciplinary field solvers of high order; optimum design of parallel solvers considering uncertainties; parallel in-solver visualization and feature extraction.
The major challenge in NUMEXAS will be the development of a new set of numerical methods and computer codes that will allow industries, governments and academia to routinely solve multidisciplinary large-scale class problems in applied sciences and engineering with high efficiency and simplicity. NUMEXAS strives to demonstrate good scalability of up to several tens of thousands of cores in practice and to predict the theoretical capability of significant further performance gains with even higher orders of numbers of cores.