MEMPower: Data-Aware GPU Memory Power Model

dc.contributor.authorLucas, Jan
dc.contributor.authorJuurlink, Ben
dc.date.accessioned2019-11-21T11:17:39Z
dc.date.available2019-11-21T11:17:39Z
dc.date.issued2019-04-25
dc.description.abstractThis paper presents the MEMPower power model. MEMPower is a detailed empirical power model for GPU memory access. It models the data dependent energy consumption as well as individual core specific differences. We explain how the model was calibrated using special micro benchmarks as well as a high-resolution power measurement testbed. A novel technique to identify the number of memory channels and the memory channel of a specific address is presented. Our results show significant differences in the access energy of specific GPU cores, while the access energy of the different memory channels from the same GPU cores is almost identical. MEMPower is able to model these differences and provide good predictions of the access energy for specific memory accesses.en
dc.description.sponsorshipEC/H2020/688759/EU/Low-Power Parallel Computing on GPUs 2/LPGPU2en
dc.identifier.isbn978-3-030-18656-2
dc.identifier.isbn978-3-030-18655-5
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/10370
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-9330
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc004 Datenverarbeitung; Informatikde
dc.subject.otherGPUen
dc.subject.othermemoryen
dc.subject.otherpower modelingen
dc.subject.otherdata dependent poweren
dc.titleMEMPower: Data-Aware GPU Memory Power Modelen
dc.typeConference Objecten
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.doi10.1007/978-3-030-18656-2_15en
dcterms.bibliographicCitation.editorSchoeberl, Martin
dcterms.bibliographicCitation.editorHochberger, Christian
dcterms.bibliographicCitation.editorUhrig, Sascha
dcterms.bibliographicCitation.editorBrehm, Jürgen
dcterms.bibliographicCitation.editorPionteck, Thilo
dcterms.bibliographicCitation.originalpublishernameSpringeren
dcterms.bibliographicCitation.originalpublisherplaceChamen
dcterms.bibliographicCitation.pageend207en
dcterms.bibliographicCitation.pagestart195en
dcterms.bibliographicCitation.proceedingstitleArchitecture of Computing Systems – ARCS 2019 : 32nd International Conference, Copenhagen, Denmark, May 20–23, 2019, Proceedingsen
tub.accessrights.dnbfreeen
tub.affiliationFak. 4 Elektrotechnik und Informatik::Inst. Technische Informatik und Mikroelektronik::FG Architektur eingebetteter Systemede
tub.affiliation.facultyFak. 4 Elektrotechnik und Informatikde
tub.affiliation.groupFG Architektur eingebetteter Systemede
tub.affiliation.instituteInst. Technische Informatik und Mikroelektronikde
tub.publisher.universityorinstitutionTechnische Universität Berlinen
tub.series.issuenumber11479en
tub.series.nameLecture Notes in Computer Scienceen

Files

Original bundle
Now showing 1 - 1 of 1
Loading…
Thumbnail Image
Name:
lucas_juurlink_2019.pdf
Size:
10.55 MB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
4.9 KB
Format:
Item-specific license agreed upon to submission
Description:

Collections