Powered by OpenAIRE graph
Found an issue? Give us feedback


Country: Spain
330 Projects, page 1 of 66
  • Funder: EC Project Code: 639595
    Overall Budget: 1,467,780 EURFunder Contribution: 1,467,780 EUR

    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.

  • Funder: EC Project Code: 262412
  • Funder: EC Project Code: 101061202
    Funder Contribution: 181,153 EUR

    Cold air outbreaks are a typical feature of the mid-latitude climate during the cold season. Their relevance relies on the threat to life caused by the long-lasting periods with abnormally low temperatures as well as on the potential of damage to crops and the occurrence of high-impact weather events such as heavy snow and low visibility during blizzards. Previous studies have documented the linkages between climate variability at high latitudes with that at mid-latitudes mainly through modulation of the storm tracks, jet streams and patterns of stationary waves which promote blocking events. The role of other components of the climate system, like sea ice, is much less understood and remains an open question. This becomes even more challenging under the current conditions of fast sea ice reduction in the Arctic and the significant trends observed in sea ice around Antarctica, which leads to an increase in uncertainty in the area of sub-seasonal to interannual climate predictions as well as for climate projections for the coming decades. Furthermore, significant biases currently exist in the representation of sea ice in state-of-the-art climate model simulations. The main objective of this proposal is to analyze the physical mechanisms linking variability at high latitudes (including that of sea ice) with climate variability at mid-latitudes. Special focus will be driven onto the mechanisms promoting cold snaps at mid-latitudes and on their variability. To this aim, this project will make use of a data set of very-high-resolution coupled global climate model simulations (at around 10 km) which is expected to bring significant improvements to the representation of sea ice as well as to its linkages with other components of the climate system. Outcomes of this project are expected to become useful for decision makers and stakeholders, as well as to researchers working in the field of climate predictions and projections.

  • Funder: EC Project Code: 846139
    Overall Budget: 172,932 EURFunder Contribution: 172,932 EUR

    High performance computing (HPC) has transformed scientific research across numerous disciplines by supporting theory and experiments with numerical simulations. Exascale computing is the next milestone in HPC and is called to play an important role in economic competitiveness, societal challenges and science leadership. Combustion is one of the fields with high strategic importance and potential to fully exploit the future exascale systems. Nowadays, combustion of fossil fuels is the main power source, and some projections indicate that the combustion of liquid fuels will still dominate transportation and power generation industries for the next 50 years. Further understanding of the physics and chemistry of the combustion process is fundamental to achieve improvements in fuel efficiency, reducing greenhouse gas emissions and pollutants, while transitioning to alternative fuels and greener technologies. The use of advanced numerical simulations has enabled to make important contributions for increasing cycle efficiency, reduction of pollutant emissions, and use of alternative fuels in practical applications. The exascale computing will enable the development of high-fidelity turbulent combustion simulations that could not be analyzed before because it was too computationally expensive. However, the implementation of the new and future supercomputers require the evolution of multiple and different technologies in a coherent and complimentary way, including hardware, software, and application algorithms. Scientific codes and formulations need to be re-designed and adapted in order to exploit the different levels of parallelism and complex memory hierarchies of the new and future heterogeneous systems. The goal of the project is to explore and develop novel co-execution, memory awareness and communication avoidance strategies into a framework that allows the simulation of advance high-fidelity multiphase reacting flows in complex geometries using unstructured grids.

  • Funder: EC Project Code: 754433
    Overall Budget: 2,832,000 EURFunder Contribution: 1,416,000 EUR

    The BSC STARS (SupercompuTing And Related applicationS) International Research Fellows program aims at fostering the training of highly skilled post-doc in all fields of High Performance Computing and related applications, specifically in Earth Science and Meteorology, in Life Sciences, Genomics and Personalised Medicine and in Computational Engineering and Physics and Computational Societies, providing them with all the necessary tools for developing their potentials, deepening their skills and knowledge in a stimulating, international and interdisciplinary environment, offering them intersectorial secondments with private industry or non-academic Research centers, in order to boost their career perspectives as successful independent researchers.

Powered by OpenAIRE graph
Found an issue? Give us feedback

Do the share buttons not appear? Please make sure, any blocking addon is disabled, and then reload the page.

Content report
No reports available
Funder report
No option selected

Do you wish to download a CSV file? Note that this process may take a while.

There was an error in csv downloading. Please try again later.