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.

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.

High performance computing has changed the way scientists make discoveries and is driving industrial innovation. From the simulation of the origins of the universe, to the optimization of wind turbine placement; supercomputers are helping us to change the world and improve the conditions of future generations. The next generation of European extreme scale computers brings new opportunities but also imposes new challenge: they need to be an order of magnitude more energy efficient and they need to be reliable so that no data is lost in the presence of failures. Novel deep-memory hierarchies offer an alternative to achieve these resilience and efficiency goals. Unfortunately, it is unclear how the system should utilize these cutting-edge hardware devices. The objective of this project is to build an abstraction layer between the new hybrid memory hardware and the scientific application, providing an easy way to leverage the features of the hardware while maintaining high energy efficiency and strong reliability. Barcelona Supercomputing Centre is an ideal place to carry out this research because of its top-level researchers. In particular, the fellowship will be supervised by Dr. Osman Unsal, who has a long outstanding experience supervising European projects. The combination of such an important project with the high quality training of the host institution represents the best career opportunity for the candidate to expand and solidify his research experience.

High-fidelity flow simulation is one of the main goals to pursue in the research towards exascale computing. This capability will allow a cheaper exploration of aeronautical and automotive designs that fulfill energy consumption and noise emissions policies of the European agencies. In this project, the integration of three high-performance tools will be studied as a promising alternative to perform high-fidelity flow simulations. Specifically, a parallel curved unstructured mesh generator will be integrated with two different parallel high-order stabilized Galerkin solvers. We expect that the novel research on the development and combination of these high-performance tools will provide the physical, numerical, and geometrical accuracy required to perform high-fidelity flow simulations on complex domains defined by industrial computer-aided design models. Furthermore, the project is of major interest in high-performance computing since we expect to improve the scalability of implicit flow solvers by increasing the accuracy for a given computational cost, favoring computation to data transfer, and increasing the ratio of operations that scale linearly with the number of mesh elements. The combined tools will be deployed in a large cluster to obtain flow simulations of practical interest for the aeronautical and automotive industries.