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Parallel Computing Methods For Particle Accelerator Design

We present methods for parallelizing the transport map construction for multi-core processors and for Graphics Processing Units (GPUs). We provide an efficient implementation of the transport map construction. We describe a method for multi-core processors using the OpenMP framework which brings per...

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Autor principal: Popescu, Diana Andreea
Lenguaje:eng
Publicado: 2013
Materias:
Acceso en línea:http://cds.cern.ch/record/1598904
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author Popescu, Diana Andreea
author_facet Popescu, Diana Andreea
author_sort Popescu, Diana Andreea
collection CERN
description We present methods for parallelizing the transport map construction for multi-core processors and for Graphics Processing Units (GPUs). We provide an efficient implementation of the transport map construction. We describe a method for multi-core processors using the OpenMP framework which brings performance improvement over the serial version of the map construction. We developed a novel and efficient algorithm for multivariate polynomial multiplication for GPUs and we implemented it using the CUDA framework. We show the benefits of using the multivariate polynomial multiplication algorithm for GPUs in the map composition operation for high orders. Finally, we present an algorithm for map composition for GPUs.
id cern-1598904
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2013
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spelling cern-15989042019-09-30T06:29:59Zhttp://cds.cern.ch/record/1598904engPopescu, Diana AndreeaParallel Computing Methods For Particle Accelerator DesignComputing and ComputersAccelerators and Storage RingsWe present methods for parallelizing the transport map construction for multi-core processors and for Graphics Processing Units (GPUs). We provide an efficient implementation of the transport map construction. We describe a method for multi-core processors using the OpenMP framework which brings performance improvement over the serial version of the map construction. We developed a novel and efficient algorithm for multivariate polynomial multiplication for GPUs and we implemented it using the CUDA framework. We show the benefits of using the multivariate polynomial multiplication algorithm for GPUs in the map composition operation for high orders. Finally, we present an algorithm for map composition for GPUs.CERN-THESIS-2013-121oai:cds.cern.ch:15989042013-09-08T22:38:26Z
spellingShingle Computing and Computers
Accelerators and Storage Rings
Popescu, Diana Andreea
Parallel Computing Methods For Particle Accelerator Design
title Parallel Computing Methods For Particle Accelerator Design
title_full Parallel Computing Methods For Particle Accelerator Design
title_fullStr Parallel Computing Methods For Particle Accelerator Design
title_full_unstemmed Parallel Computing Methods For Particle Accelerator Design
title_short Parallel Computing Methods For Particle Accelerator Design
title_sort parallel computing methods for particle accelerator design
topic Computing and Computers
Accelerators and Storage Rings
url http://cds.cern.ch/record/1598904
work_keys_str_mv AT popescudianaandreea parallelcomputingmethodsforparticleacceleratordesign