Spatial and Temporal Cache Sharing Analysis in Tasks

Ceballos, Germán; Black-Schaffer, David

Publication:
Spatial and Temporal Cache Sharing Analysis in Tasks

dc.affiliation.dpto	UC3M. Departamento de Informática	es
dc.affiliation.grupoinv	UC3M. Grupo de Investigación: Arquitectura de Computadores, Comunicaciones y Sistemas	es
dc.contributor.author	Ceballos, Germán
dc.contributor.author	Black-Schaffer, David
dc.contributor.editor	Carretero Pérez, Jesús
dc.contributor.editor	García Blas, Javier
dc.contributor.editor	Petcu, Dana
dc.date.accessioned	2016-04-29T08:10:35Z
dc.date.available	2016-04-29T08:10:35Z
dc.date.issued	2016-02
dc.description	Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.	en
dc.description.abstract	Understanding performance of large scale multicore systems is crucial for getting faster execution times and optimize workload efficiency, but it is becoming harder due to the increased complexity of hardware architectures. Cache sharing is a key component for performance in modern architectures, and it has been the focus of performance analysis tools and techniques in recent years. At the same time, new programming models have been introduced to aid the programmer dealing with the complexity of large scale systems, simplifying the coding process and making applications more scalable regardless of resource sharing. Taskbased runtime systems are one example of this that became popular recently. In this work we develop models to tackle performance analysis of shared resources in the task-based context, and for that we study cache sharing both in temporal and spatial ways. In temporal cache sharing, the effect of data reused over time by the tasks executed is modeled to predict different scenarios resulting in a tool called StatTask. In spatial cache sharing, the effect of tasks fighting for the cache at a given point in time through their execution is quantified and used to model their behavior on arbitrary cache sizes. Finally, we explain how these tools set up a unique and solid platform to improve runtime systems schedulers, maximizing performance of execution of large-scale task-based applications.	en
dc.description.sponsorship	European Cooperation in Science and Technology. COST	en
dc.description.sponsorship	The work presented in this paper has been partially supported by EU under the COST programme Action IC1305,‘Network for Sustainable Ultrascale Computing (NESUS)’, and by the Swedish Research Council, carried out within the Linnaeus centre of excellence UPMARC, Uppsala Programming for Multicore Architectures Research Center.	en
dc.format.extent	5
dc.format.mimetype	application/pdf
dc.identifier.bibliographicCitation	Carretero Pérez, Jesús; et.al. (eds.). (2016). Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016). Timisoara, Romania. Universidad Carlos III de Madrid, ARCOS. Pp. 59-63.	en
dc.identifier.isbn	978-84-608-6309-0
dc.identifier.publicationfirstpage	59
dc.identifier.publicationlastpage	63
dc.identifier.publicationtitle	Proceedings of the First PhD Symposium on Sustainable UltrascaleComputing Systems (NESUS PhD 2016)	en
dc.identifier.uri	https://hdl.handle.net/10016/22886
dc.language.iso	eng
dc.relation.eventdate	February 8-11, 2016	en
dc.relation.eventnumber	1
dc.relation.eventplace	Timisoara, Romania	en
dc.relation.eventtitle	PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016)	en
dc.rights	Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRights	open access
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.eciencia	Informática	es
dc.subject.other	Task-based runtime systems	en
dc.subject.other	Cache sharing	en
dc.subject.other	Performance analysis	en
dc.subject.other	NESUS	en
dc.title	Spatial and Temporal Cache Sharing Analysis in Tasks	en
dc.type	conference paper	*
dc.type.hasVersion	VoR	*
dspace.entity.type	Publication