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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...
Autores principales: | , |
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Lenguaje: | eng |
Publicado: |
2015
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1088/1742-6596/664/9/092024 http://cds.cern.ch/record/2134653 |
_version_ | 1780949924669554688 |
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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 AT innocentev evaluatingthepowerefficiencyandperformanceofmulticoreplatformsusinghepworkloads |