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Lookup NU author(s): Mehmet CengizORCiD, Dr Matthew ForshawORCiD, Dr Amir Atapour AbarghoueiORCiD, Dr Stephen McGough
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Predicting the performance and energy consumption of computing hardware is critical for many modern applications. This will inform procurement decisions, deployment decisions, and autonomic scaling. Existing approaches to understanding the performance of hardware largely focus around benchmarking -- leveraging standardised workloads which seek to be representative of an end-user’s needs. Two key challenges are present; benchmark workloads may not be representative of an end-user’s workload, and benchmark scores are not easily obtained for all hardware. Within this paper, we demonstrate the potential to build Deep Learning models to predict benchmark scores for unseen hardware. We undertake our evaluation with the openly available SPEC 2017 benchmark results. We evaluate three different networks, one fully-connected network along with two Convolutional Neural Networks (one bespoke and one ResNet inspired) and demonstrate impressive $R^2$ scores of 0.96, 0.98 and 0.94 respectively.
Author(s): Cengiz M, Forshaw M, Atapour-Abarghouei A, McGough AS
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: ACM/SPEC International Conference on Performance Engineering
Year of Conference: 2023
Pages: 91-98
Print publication date: 15/04/2023
Online publication date: 15/04/2023
Acceptance date: 29/12/2022
Date deposited: 20/04/2023
Publisher: Association for Computing Machinery
URL: https://dl.acm.org/doi/10.1145/3578244.3583731
DOI: 10.1145/3578244.3583731
Library holdings: Search Newcastle University Library for this item
Series Title: International Conference on Performance Engineering
Sponsor(s): SIGMETRICS, SIGSOFT
ISBN: 9798400700682
