New technologies to bridge the gap between High Performance Computing (HPC) and Big Data

The unification of HPC and Big Data has received increasing attention in the last years. It is a common belief that exascale computing and Big Data are closely associated since HPC requires processing large-scale data from scientific instruments and simulations. But, at the same time, it was observe...

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Detalles Bibliográficos
Autor: Piñeiro Pomar, César Alfredo
Tipo de recurso: tesis doctoral
Fecha de publicación:2022
País:España
Institución:Universidad de Santiago de Compostela (USC)
Repositorio:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
Idioma:inglés
OAI Identifier:oai:minerva.usc.gal:10347/29947
Acceso en línea:http://hdl.handle.net/10347/29947
Access Level:acceso abierto
Palabra clave:Materias::Investigación::33 Ciencias tecnológicas::3304 Tecnología de los ordenadores::330406 Arquitectura de ordenadores
Descripción
Sumario:The unification of HPC and Big Data has received increasing attention in the last years. It is a common belief that exascale computing and Big Data are closely associated since HPC requires processing large-scale data from scientific instruments and simulations. But, at the same time, it was observed that tools and cultures of HPC and Big Data communities differ significantly. One of the most important issues in the path to the convergence is caused by the differences in their software stacks. This thesis will address the research challenge of bridging the gap between Big Data and HPC worlds. With this goal in mind, a set of tools and technologies will be developed and integrated into a new unified Big Data-HPC framework that will allow the execution of scientific multi-language applications on both environments using containers.

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