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International projects source: SICRIS

High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)

Researchers (2)
no. Code Name and surname Research area Role Period No. of publicationsNo. of publications
1.  11077  PhD Denis Trček  Computer science and informatics  Researcher  2015 - 2019  323 
2.  06795  PhD Mira Trebar  Computer science and informatics  Researcher  2015 - 2019  197 
Organisations (1)
no. Code Research organisation City Registration number No. of publicationsNo. of publications
1.  1539  University of Ljubljana, Faculty of Computer and Information Science  Ljubljana  1627023  16,242 
Abstract
The Big Data era poses a critically difficult challenge and striking development opportunities in High-Performance Computing (HPC): how to efficiently turn massively large data into valuable information and meaningful knowledge. Computationally effective HPC is required in a rapidly-increasing number of data-intensive domains, such as Life and Physical Sciences, and Socio-economical Systems.Modelling and Simulation (MS) offer suitable abstractions to manage the complexity of analysing Big Data in various scientific and engineering domains. Unfortunately, Big Data problems are not always easily amenable to efficient MS over HPC. Also, MS communities may lack the detailed expertise required to exploit the full potential of HPC solutions, and HPC architects may not be fully aware of specific MS requirements.Therefore, there is an urgent need for European co-ordination to facilitate interactions among data intensive MS and HPC experts, ensuring that the field, which is strategic and of long-standing interest in Europe, develops efficiently - from academic research to industrial practice. This Action will provide the integration to foster a novel, coordinated Big Data endeavour supported by HPC. It will strongly support information exchange, synergy and coordination of activities among leading European research groups and top global partner institutions, and will promote European software industry competitiveness.
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