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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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Lenguaje: | eng |
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2013
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Acceso en línea: | http://cds.cern.ch/record/1598904 |
_version_ | 1780931316582187008 |
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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 |
record_format | invenio |
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 |