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dc.date.accessioned 2017-12-05T17:39:05Z
dc.date.available 2017-12-05T17:39:05Z
dc.date.issued 2017-10
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/63943
dc.description.abstract It is known that energy and power consumption are becoming serious metrics in the design of high performance workstations because of heat dissipation problems. In the last years, GPU accelerators have been integrating many of these expensive systems despite they are embedding more and more transistors on their chips producing a quick increase of power consumption requirements. This paper analyzes an image processing application, in particular a Discrete Cosine Transform denoising algorithm, in terms of CPU and GPU performance and energy consumption. Specifically, we want to compare single-threaded and multithreaded CPU versions with a GPU version, and characterize the execution time, true instant power and average energy consumption to deflate the idea that GPUs are non-green computing devices. en
dc.format.extent 185-195 es
dc.language en es
dc.subject denoising en
dc.subject power en
dc.subject energy en
dc.subject GPU en
dc.subject NVML en
dc.subject RAPL en
dc.title GPU Performance and Power Consumption Analysis: A DCT based denoising application es
dc.type Objeto de conferencia es
sedici.identifier.isbn 978-950-34-1539-9 es
sedici.creator.person Pi Puig, Martín es
sedici.creator.person De Giusti, Laura Cristina es
sedici.creator.person Naiouf, Marcelo es
sedici.creator.person De Giusti, Armando Eduardo es
sedici.description.note XVIII Workshop de Procesamiento Distribuido y Paralelo (WPDP). es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Red de Universidades con Carreras en Informática (RedUNCI) es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/4.0/
sedici.date.exposure 2017-10
sedici.relation.event XXIII Congreso Argentino de Ciencias de la Computación (La Plata, 2017). es
sedici.description.peerReview peer-review es


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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)