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Hardware-validated CPU performance and energy modelling

Hardware-validated CPU performance and energy modelling
Hardware-validated CPU performance and energy modelling
Full-system simulation frameworks such as gem5 are used extensively to evaluate research ideas and for design-space exploration. Moreover, energy-efficiency has become the key design constraint in recent years and many works use a separate power modelling framework to evaluate energy consumption. While such tools are convenient and flexible, they are known to contain sources of error which are often not fully understood and potentially impact the conclusions drawn from investigations. This work enables accurate, hardware-validated performance, power, and energy modelling of CPUs by first presenting a methodology to evaluate and identify sources of error in CPU performance models, and secondly developing empirical power models optimised for use with such performance models. Hierarchical clustering, correlation analysis, and regression techniques are used to identify sources of error without requiring detailed CPU specifications and enable existing models to be improved, new models to be developed, validation of simulator changes, and testing of model suitability for specific use-cases. Furthermore, the GemStone open-source software tool is presented, which automates the process of characterising hardware platforms, identifying sources of error in gem5 models, applying power analysis, and quantifying the effect of errors on the performance, power, and energy estimations. In addition, the mean percentage error in execution time was found to swing from −51% to +10% between two versions of the same gem5 model, underlining the need for an automated tool to validate models against reference hardware, ensuring accuracy and consistency.
Walker, Matthew
77e58c74-1541-4ffc-9219-4c8c11248a2e
Bischoff, Sascha
3afa2642-9501-498e-8d19-04e0cc5c0af6
Diestelhorst, Stephan
80286a84-4bcb-432e-9557-96bc4063df63
Merrett, Geoff
89b3a696-41de-44c3-89aa-b0aa29f54020
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d
Walker, Matthew
77e58c74-1541-4ffc-9219-4c8c11248a2e
Bischoff, Sascha
3afa2642-9501-498e-8d19-04e0cc5c0af6
Diestelhorst, Stephan
80286a84-4bcb-432e-9557-96bc4063df63
Merrett, Geoff
89b3a696-41de-44c3-89aa-b0aa29f54020
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d

Walker, Matthew, Bischoff, Sascha, Diestelhorst, Stephan, Merrett, Geoff and Al-Hashimi, Bashir (2018) Hardware-validated CPU performance and energy modelling. At 2018 IEEE International Symposium on Performance Analysis of Systems and Software (04/04/18) 2018 IEEE International Symposium on Performance Analysis of Systems and Software, Belfast, United Kingdom. 02 - 04 Apr 2018. 10 pp.

Record type: Conference or Workshop Item (Paper)

Abstract

Full-system simulation frameworks such as gem5 are used extensively to evaluate research ideas and for design-space exploration. Moreover, energy-efficiency has become the key design constraint in recent years and many works use a separate power modelling framework to evaluate energy consumption. While such tools are convenient and flexible, they are known to contain sources of error which are often not fully understood and potentially impact the conclusions drawn from investigations. This work enables accurate, hardware-validated performance, power, and energy modelling of CPUs by first presenting a methodology to evaluate and identify sources of error in CPU performance models, and secondly developing empirical power models optimised for use with such performance models. Hierarchical clustering, correlation analysis, and regression techniques are used to identify sources of error without requiring detailed CPU specifications and enable existing models to be improved, new models to be developed, validation of simulator changes, and testing of model suitability for specific use-cases. Furthermore, the GemStone open-source software tool is presented, which automates the process of characterising hardware platforms, identifying sources of error in gem5 models, applying power analysis, and quantifying the effect of errors on the performance, power, and energy estimations. In addition, the mean percentage error in execution time was found to swing from −51% to +10% between two versions of the same gem5 model, underlining the need for an automated tool to validate models against reference hardware, ensuring accuracy and consistency.

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More information

Accepted/In Press date: 19 January 2018
Published date: 3 April 2018
Venue - Dates: 2018 IEEE International Symposium on Performance Analysis of Systems and Software, Belfast, United Kingdom, 2018-04-02 - 2018-04-04

Identifiers

Local EPrints ID: 418538
URI: https://eprints.soton.ac.uk/id/eprint/418538
PURE UUID: 44aa9f29-1ec7-47f0-b813-0dc87e7227d1
ORCID for Matthew Walker: ORCID iD orcid.org/0000-0001-6368-3644
ORCID for Geoff Merrett: ORCID iD orcid.org/0000-0003-4980-3894

Catalogue record

Date deposited: 09 Mar 2018 17:31
Last modified: 06 Oct 2018 00:35

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