Cargando…

Vectorising the detector geometry to optimize particle transport

Among the components contributing to particle transport, geometry navigation is an important consumer of CPU cycles. The tasks performed to get answers to "basic" queries such as locating a point within a geometry hierarchy or computing accurately the distance to the next boundary can beco...

Descripción completa

Detalles Bibliográficos
Autores principales: Apostolakis, John, Brun, René, Carminati, Federico, Gheata, Andrei, Wenzel, Sandro
Lenguaje:eng
Publicado: 2013
Materias:
Acceso en línea:https://dx.doi.org/10.1088/1742-6596/513/5/052038
http://cds.cern.ch/record/1633262
_version_ 1780934408978563072
author Apostolakis, John
Brun, René
Carminati, Federico
Gheata, Andrei
Wenzel, Sandro
author_facet Apostolakis, John
Brun, René
Carminati, Federico
Gheata, Andrei
Wenzel, Sandro
author_sort Apostolakis, John
collection CERN
description Among the components contributing to particle transport, geometry navigation is an important consumer of CPU cycles. The tasks performed to get answers to "basic" queries such as locating a point within a geometry hierarchy or computing accurately the distance to the next boundary can become very computing intensive for complex detector setups. So far, the existing geometry algorithms employ mainly scalar optimisation strategies (voxelization, caching) to reduce their CPU consumption. In this paper, we would like to take a different approach and investigate how geometry navigation can benefit from the vector instruction set extensions that are one of the primary source of performance enhancements on current and future hardware. While on paper, this form of microparallelism promises increasing performance opportunities, applying this technology to the highly hierarchical and multiply branched geometry code is a difficult challenge. We refer to the current work done to vectorise an important part of the critical navigation algorithms in the ROOT geometry library. Starting from a short critical discussion about the programming model, we present the current status and first benchmark results of the vectorisation of some elementary geometry shape algorithms. On the path towards a full vector-based geometry navigator, we also investigate the performance benefits in connecting these elementary functions together to develop algorithms which are entirely based on the flow of vector-data. To this end, we discuss core components of a simple vector navigator that is tested and evaluated on a toy detector setup.
id cern-1633262
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2013
record_format invenio
spelling cern-16332622022-08-17T13:29:16Zdoi:10.1088/1742-6596/513/5/052038http://cds.cern.ch/record/1633262engApostolakis, JohnBrun, RenéCarminati, FedericoGheata, AndreiWenzel, SandroVectorising the detector geometry to optimize particle transportOther Fields of PhysicsAmong the components contributing to particle transport, geometry navigation is an important consumer of CPU cycles. The tasks performed to get answers to "basic" queries such as locating a point within a geometry hierarchy or computing accurately the distance to the next boundary can become very computing intensive for complex detector setups. So far, the existing geometry algorithms employ mainly scalar optimisation strategies (voxelization, caching) to reduce their CPU consumption. In this paper, we would like to take a different approach and investigate how geometry navigation can benefit from the vector instruction set extensions that are one of the primary source of performance enhancements on current and future hardware. While on paper, this form of microparallelism promises increasing performance opportunities, applying this technology to the highly hierarchical and multiply branched geometry code is a difficult challenge. We refer to the current work done to vectorise an important part of the critical navigation algorithms in the ROOT geometry library. Starting from a short critical discussion about the programming model, we present the current status and first benchmark results of the vectorisation of some elementary geometry shape algorithms. On the path towards a full vector-based geometry navigator, we also investigate the performance benefits in connecting these elementary functions together to develop algorithms which are entirely based on the flow of vector-data. To this end, we discuss core components of a simple vector navigator that is tested and evaluated on a toy detector setup.arXiv:1312.0816oai:cds.cern.ch:16332622013-12-03
spellingShingle Other Fields of Physics
Apostolakis, John
Brun, René
Carminati, Federico
Gheata, Andrei
Wenzel, Sandro
Vectorising the detector geometry to optimize particle transport
title Vectorising the detector geometry to optimize particle transport
title_full Vectorising the detector geometry to optimize particle transport
title_fullStr Vectorising the detector geometry to optimize particle transport
title_full_unstemmed Vectorising the detector geometry to optimize particle transport
title_short Vectorising the detector geometry to optimize particle transport
title_sort vectorising the detector geometry to optimize particle transport
topic Other Fields of Physics
url https://dx.doi.org/10.1088/1742-6596/513/5/052038
http://cds.cern.ch/record/1633262
work_keys_str_mv AT apostolakisjohn vectorisingthedetectorgeometrytooptimizeparticletransport
AT brunrene vectorisingthedetectorgeometrytooptimizeparticletransport
AT carminatifederico vectorisingthedetectorgeometrytooptimizeparticletransport
AT gheataandrei vectorisingthedetectorgeometrytooptimizeparticletransport
AT wenzelsandro vectorisingthedetectorgeometrytooptimizeparticletransport