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Use of hardware accelerators for ATLAS computing
Modern HEP experiments produce tremendous amounts of data. This data is processed by in-house built software frameworks which have lifetimes longer than the detector it- self. Such frameworks were traditionally based on serial code and relied on advances in CPU technologies, mainly clock frequency,...
Autores principales: | , , , , |
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Lenguaje: | eng |
Publicado: |
2014
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.3204/DESY-PROC-2014-05/10 http://cds.cern.ch/record/1970398 |
Sumario: | Modern HEP experiments produce tremendous amounts of data. This data is processed by in-house built software frameworks which have lifetimes longer than the detector it- self. Such frameworks were traditionally based on serial code and relied on advances in CPU technologies, mainly clock frequency, to cope with increasing data volumes. With the advent of many-core architectures and GPGPUs this paradigm has to shift to paral- lel processing and has to include the use of co-processors. However, since the design of most existing frameworks is based on the assumption of frequency scaling and predate co-processors, parallelisation and integration of co-processors are not an easy task. The ATLAS experiment is an example of such a big experiment with a big software frame- work called Athena. In this proceedings we will present the studies on parallelisation and co-processor (GPGPU) use in data preparation and tracking for trigger and offline recon- struction as well as their integration into a multiple process based Athena framework. |
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