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dc.contributor.authorKaszyk, Jakub
dc.contributor.authorWagstaff, Harry
dc.contributor.authorSpink, Tom
dc.contributor.authorFranke, Bjoern
dc.contributor.authorO'Boyle, Michael
dc.contributor.authorBodin, Bruno
dc.contributor.authorUhrenholt, Henrik
dc.date.accessioned2021-11-11T17:30:18Z
dc.date.available2021-11-11T17:30:18Z
dc.date.issued2019-04-25
dc.identifier.citationKaszyk , J , Wagstaff , H , Spink , T , Franke , B , O'Boyle , M , Bodin , B & Uhrenholt , H 2019 , Full-system simulation of mobile CPU/GPU platforms . in 2019 IEEE International Symposium on Performance Analysis of Systems and Software . Institute of Electrical and Electronics Engineers (IEEE) , pp. 68-78 . https://doi.org/10.1109/ISPASS.2019.00015en
dc.identifier.isbn9781728107479
dc.identifier.isbn9781728107462
dc.identifier.otherPURE: 276650566
dc.identifier.otherPURE UUID: 98c52ba2-6234-4ac7-9d68-3857b17382c1
dc.identifier.otherRIS: urn:42B5AA7FFF660754AB6CA8FA23AC7B0E
dc.identifier.otherScopus: 85065399606
dc.identifier.otherORCID: /0000-0002-7662-3146/work/103138173
dc.identifier.urihttp://hdl.handle.net/10023/24324
dc.description.abstractGraphics Processing Units (GPUs) critically rely on a complex system software stack comprising kernel- and userspace drivers and Just-in-time (JIT) compilers. Yet, existing GPU simulators typically abstract away details of the software stack and GPU instruction set. Partly, this is because GPU vendors rarely release sufficient information about their latest GPU products. However, this is also due to the lack of an integrated CPU/GPU simulation framework, which is complete and powerful enough to drive the complex GPU software environment. This has led to a situation where research on GPU architectures and compilers is largely based on outdated or greatly simplified architectures and software stacks, undermining the validity of the generated results. In this paper we develop a full-system system simulation environment for a mobile platform, which enables users to run a complete and unmodified software stack for a state-of-the-art mobile Arm CPU and Mali-G71 GPU powered device. We validate our simulator against a hardware implementation and Arm’s stand-alone GPU simulator, achieving 100% architectural accuracy across all available toolchains. We demonstrate the capability of our GPU simulation framework by optimizing an advanced Computer Vision application using simulated statistics unavailable with other simulation approaches or physical GPU implementations. We demonstrate that performance optimizations for desktop GPUs trigger bottlenecks on mobile GPUs, and show the importance of efficient memory use.
dc.format.extent11
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof2019 IEEE International Symposium on Performance Analysis of Systems and Softwareen
dc.rightsCopyright © 2019 IEEE. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1109/ISPASS.2019.00015.en
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectT-NDASen
dc.subject.lccQA75en
dc.titleFull-system simulation of mobile CPU/GPU platformsen
dc.typeConference itemen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.identifier.doihttps://doi.org/10.1109/ISPASS.2019.00015


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