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Evaluating the power efficiency and performance of multi-core platforms using HEP workloads

As Moore's Law drives the silicon industry towards higher transistor counts, processor designs are becoming more and more complex. The area of development includes core count, execution ports, vector units, uncore architecture and finally instruction sets. This increasing complexity leads us to...

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Detalles Bibliográficos
Autores principales: Szostek, P, Innocente, V
Lenguaje:eng
Publicado: 2015
Materias:
Acceso en línea:https://dx.doi.org/10.1088/1742-6596/664/9/092024
http://cds.cern.ch/record/2134653
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author Szostek, P
Innocente, V
author_facet Szostek, P
Innocente, V
author_sort Szostek, P
collection CERN
description As Moore's Law drives the silicon industry towards higher transistor counts, processor designs are becoming more and more complex. The area of development includes core count, execution ports, vector units, uncore architecture and finally instruction sets. This increasing complexity leads us to a place where access to the shared memory is the major limiting factor, resulting in feeding the cores with data a real challenge. On the other hand, the significant focus on power efficiency paves the way for power-aware computing and less complex architectures to data centers. In this paper we try to examine these trends and present results of our experiments with Haswell-EP processor family and highly scalable HEP workloads.
id oai-inspirehep.net-1414099
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2015
record_format invenio
spelling oai-inspirehep.net-14140992022-08-10T13:01:08Zdoi:10.1088/1742-6596/664/9/092024http://cds.cern.ch/record/2134653engSzostek, PInnocente, VEvaluating the power efficiency and performance of multi-core platforms using HEP workloadsComputing and ComputersAs Moore's Law drives the silicon industry towards higher transistor counts, processor designs are becoming more and more complex. The area of development includes core count, execution ports, vector units, uncore architecture and finally instruction sets. This increasing complexity leads us to a place where access to the shared memory is the major limiting factor, resulting in feeding the cores with data a real challenge. On the other hand, the significant focus on power efficiency paves the way for power-aware computing and less complex architectures to data centers. In this paper we try to examine these trends and present results of our experiments with Haswell-EP processor family and highly scalable HEP workloads.oai:inspirehep.net:14140992015
spellingShingle Computing and Computers
Szostek, P
Innocente, V
Evaluating the power efficiency and performance of multi-core platforms using HEP workloads
title Evaluating the power efficiency and performance of multi-core platforms using HEP workloads
title_full Evaluating the power efficiency and performance of multi-core platforms using HEP workloads
title_fullStr Evaluating the power efficiency and performance of multi-core platforms using HEP workloads
title_full_unstemmed Evaluating the power efficiency and performance of multi-core platforms using HEP workloads
title_short Evaluating the power efficiency and performance of multi-core platforms using HEP workloads
title_sort evaluating the power efficiency and performance of multi-core platforms using hep workloads
topic Computing and Computers
url https://dx.doi.org/10.1088/1742-6596/664/9/092024
http://cds.cern.ch/record/2134653
work_keys_str_mv AT szostekp evaluatingthepowerefficiencyandperformanceofmulticoreplatformsusinghepworkloads
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