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A cluster computer performance predictor for memory scheduling

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A cluster computer performance predictor for memory scheduling

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dc.contributor.author Serrano Gómez, Mónica es_ES
dc.contributor.author Sahuquillo Borrás, Julio es_ES
dc.contributor.author Hassan Mohamed, Houcine es_ES
dc.contributor.author Petit Martí, Salvador Vicente es_ES
dc.contributor.author Duato Marín, José Francisco es_ES
dc.date.accessioned 2014-03-04T11:44:37Z
dc.date.issued 2011
dc.identifier.isbn 978-3-642-24668-5
dc.identifier.issn 0302-9743
dc.identifier.uri http://hdl.handle.net/10251/36140
dc.description.abstract Remote Memory Access (RMA) hardware allow a given motherboard in a cluster to directly access the memory installed in a remote motherboard of the same cluster. In recent works, this characteristic has been used to extend the addressable memory space of selected motherboards, which enable a better balance of main memory resources among cluster applications. This way is much more cost-effective than than implementing a full-fledged shared memory system. In this context, the memory scheduler is in charge of finding a suitable distribution of local and remote memory that maximizes the performance and guarantees a minimum QoS among the applications. Note that since changing the memory distribution is a slow process involving several motherboards, the memory scheduler needs to make sure that the target distribution provides better performance than the current one. In this paper, a performance predictor is designed in order to find the best memory distribution for a given set of applications executing in a cluster motherboard. The predictor uses simple hardware counters to estimate the expected impact on performance of the different memory distributions. The hardware counters provide the predictor with the information about the time spent in processor, memory access and network. The performance model used by the predictor has been validated in a detailed microarchitectural simulator using real benchmarks. Results show that the prediction accuracy never deviates more than 5% compared to the real results, being less than 0.5% in most of the cases. es_ES
dc.format.extent 10 es_ES
dc.language Inglés es_ES
dc.publisher Springer Verlag (Germany) es_ES
dc.relation Spanish CICYT [TIN2009-14475-C04-01] es_ES
dc.relation Consolider-Ingenio [CSD2006-00046] es_ES
dc.relation.ispartof Algorithms and Architectures for Parallel Processing es_ES
dc.relation.ispartofseries Lecture Notes in Computer Science;vol. 7017
dc.rights Reserva de todos los derechos es_ES
dc.subject Cluster computers es_ES
dc.subject Memory scheduling es_ES
dc.subject Remote memory assignment es_ES
dc.subject Performance estimation es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title A cluster computer performance predictor for memory scheduling es_ES
dc.type Capítulo de libro es_ES
dc.embargo.lift 10000-01-01
dc.embargo.terms forever es_ES
dc.identifier.doi 10.1007/978-3-642-24669-2_34
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.description.bibliographicCitation Serrano Gómez, M.; Sahuquillo Borrás, J.; Hassan Mohamed, H.; Petit Martí, SV.; Duato Marín, JF. (2011). A cluster computer performance predictor for memory scheduling. En Algorithms and Architectures for Parallel Processing. Springer Verlag (Germany). 7017:353-362. doi:10.1007/978-3-642-24669-2_34 es_ES
dc.description.accrualMethod Senia es_ES
dc.relation.conferencename 11th International Conference, ICA300 2011 es_ES
dc.relation.conferencedate October 24-26, 2011 es_ES
dc.relation.conferenceplace Melbourne, Australia es_ES
dc.relation.publisherversion http://link.springer.com/chapter/10.1007/978-3-642-24669-2_34 es_ES
dc.description.upvformatpinicio 353 es_ES
dc.description.upvformatpfin 362 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 7017 es_ES
dc.relation.senia 221014


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