To achieve high performance and high energy efficiency on near-future exascale computing systems, three key technology gaps needs to be bridged. These gaps include: energy efficiency and thermal control; extreme computation efficiency via HW acceleration and new arithmetics; methods and tools for seamless integration of reconfigurable accelerators in heterogeneous HPC multi-node platforms. TEXTAROSSA aims at tackling this gap through a co-design approach to heterogeneous HPC solutions, supported by the integration and extension of HW and SW IPs, programming models and tools derived from European research.

The High Energy Physics (HEP) Experiments at Particle Colliders need complex computing infrastructures in order to extract knowledge from the large datasets collected, with over 1 Exabyte of data stored by the experiments by now. The computing needs from the top world machine, the Large Hadron Collider (LHC) at CERN/Geneva, have seeded the realisation of the large scale GRID R&D and deployment efforts during the first decade of 2000, a posteriori proven to be adequate for the LHC data processing. The upcoming upgrade of the LHC collider, called High Luminosity LHC (HL-LHC) is foreseen to require an increase in computing resources by a factor between 10x and 100x, currently expected to be beyond the scalability of the existing distributed infrastructure. Current lines of R&D are presented and discussed. With the start of big scientific endeavours with a computing complexity similar to HL-LHC (SKA, CTA, Dune, ...) they are expected to be valid for science fields outside HEP.

In the CMS [1] computing model the experiment owns dedicated resources around the world that, for the most part, are located in computing centers with a well defined Tier hierarchy. The geo-distributed storage is then controlled centrally by the CMS Computing Operations. In this architecture data are distributed and replicated across the centers following a preplacement model, mostly human controlled. Analysis jobs are then mostly executed on computing resources close to the data location. This of course allow to avoid CPU wasting due to I/O latency, although it does not allow to optimize the available job slots.