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Grant
Agency: Cordis | Branch: H2020 | Program: RIA | Phase: FETHPC-1-2014 | Award Amount: 3.12M | Year: 2015

Energy-efficient heterogeneous supercomputing architectures need to be coupled with a radically new software stack capable of exploiting the benefits offered by the heterogeneity at all the different levels (supercomputer, job, node) to meet the scalability and energy efficiency required by Exascale supercomputers. ANTAREX will solve these challenging problems by proposing a disruptive holistic approach spanning all the decision layers composing the supercomputer software stack and exploiting effectively the full system capabilities (including heterogeneity and energy management). The main goal of the ANTAREX project is to provide a breakthrough approach to express application self-adaptivity at design-time and to runtime manage and autotune applications for green and heterogenous High Performance Computing (HPC) systems up to the Exascale level.


Grant
Agency: Cordis | Branch: H2020 | Program: RIA | Phase: EINFRA-4-2014 | Award Amount: 16.46M | Year: 2015

PRACE, the Partnership for Advanced Computing, was established in May 2010 as a permanent pan-European High Performance Computing service providing world-class systems for world-class science. Six systems at the highest performance level (Tier-0) are deployed by Germany, France, Italy and Spain providing researchers with over 9 billion core hours of compute time. HPC experts from twenty-five member states - funded in part in three implementation projects - enabled users from academia and industry to ascertain leadership and remain competitive in the Global Race. Currently PRACE is preparing for PRACE 2.0, the successor of the initial five year period. The objectives of PRACE-4IP are to build on and seamlessly continue the successes of PRACE and start new innovative and collaborative activities proposed by the consortium. These include: assisting the transition to PRACE 2.0; strengthening the internationally recognised PRACE brand; continuing advanced training which so far provided more than 15.000 person-training days to over 4700 persons, preparing strategies and best practices towards exascale computing, coordinating and enhancing the operation of the multi-tier HPC systems and services, and supporting users to exploit massively parallel systems and novel architectures. The proven project structure will be used to achieve each of the objectives in six dedicated work packages. The project will continue to be managed by Jlich. The activities are designed to increase Europes research and innovation potential especially through: seamless and efficient Tier-0 services and a pan-European HPC ecosystem including national capabilities; promoting take-up by industry and special offers to SMEs; analysing new flexible business models for PRACE 2.0; proposing strategies for deployment of leadership systems; collaborating with the ETP4HPC, the coming CoEs and other European and international organisations on future architectures, training, application support and policies.


Grant
Agency: Cordis | Branch: H2020 | Program: CSA | Phase: EINFRA-6-2014 | Award Amount: 2.00M | Year: 2015

Network of HPC Competence Centres for SMEs


Grant
Agency: Cordis | Branch: H2020 | Program: RIA | Phase: FETHPC-1-2014 | Award Amount: 3.91M | Year: 2015

Scalable machine learning of complex models on extreme data will be an important industrial application of exascale computers. In this project, we take the example of predicting compound bioactivity for the pharmaceutical industry, an important sector for Europe for employment, income, and solving the problems of an ageing society. Small scale approaches to machine learning have already been trialed and show great promise to reduce empirical testing costs by acting as a virtual screen to filter out tests unlikely to work. However, it is not yet possible to use all available data to make the best possible models, as algorithms (and their implementations) capable of learning the best models do not scale to such sizes and heterogeneity of input data. There are also further challenges including imbalanced data, confidence estimation, data standards model quality and feature diversity. The ExCAPE project aims to solve these problems by producing state of the art scalable algorithms and implementations thereof suitable for running on future Exascale machines. These approaches will scale programs for complex pharmaceutical workloads to input data sets at industry scale. The programs will be targeted at exascale platforms by using a mix of HPC programming techniques, advanced platform simulation for tuning and and suitable accelerators.


Grant
Agency: Cordis | Branch: H2020 | Program: RIA | Phase: FETHPC-1-2014 | Award Amount: 3.53M | Year: 2015

High Performance Computing (HPC) has become a major instrument for many scientific and industrial fields to generate new insights and product developments. There is a continuous demand for growing compute power, leading to a constant increase in system size and complexity. Efficiently utilizing the resources provided on Exascale systems will be a challenging task, potentially causing a large amount of underutilized resources and wasted energy. Parameters for adjusting the system to application requirements exist both on the hardware and on the system software level but are mostly unused today. Moreover, accelerators and co-processors offer a significant performance improvement at the cost of increased overhead, e.g., for data-transfers. While HPC applications are usually highly compute intensive, they also exhibit a large degree of dynamic behaviour, e.g., the alternation between communication phases and compute kernels. Manually detecting and leveraging this dynamism to improve energy-efficiency is a tedious task that is commonly neglected by developers. However, using an automatic optimization approach, application dynamism can be detected at design-time and used to generate optimized system configurations. A light-weight run-time system will then detect this dynamic behaviour in production and switch parameter configurations if beneficial for the performance and energy-efficiency of the application. The READEX project will develop an integrated tool-suite and the READEX Programming Paradigm to exploit application domain knowledge, together achieving an improvement in energy-efficiency of up to 22.5%. Driven by a consortium of European experts from academia, HPC resource providers, and industry, the READEX project will develop a tools-aided methodology to exploit the dynamic behaviour of applications to achieve improved energy-efficiency and performance. The developed tool-suite will be efficient and scalable to support current and future extreme scale systems.

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