Full-system simulation of mobile CPU/GPU platforms
MetadataShow full item record
Altmetrics Handle Statistics
Altmetrics DOI Statistics
Graphics 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 This has led to a situation where research on GPU architectures and compilers is largely based on outdated or greatly simplified architectures and software stacks, undermining the validity of the generated results.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.
Kaszyk , 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.2019.00015
2019 IEEE International Symposium on Performance Analysis of Systems and Software
Copyright © 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.
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.