Predicting the Performance of a Computing System with Deep Networks

Cengiz, Mehmet; Forshaw, Matthew; Atapour-Abarghouei, Amir; McGough, Andrew Stephen

Abstract

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.

Citation

Cengiz, M., Forshaw, M., Atapour-Abarghouei, A., & McGough, A. S. (2023). Predicting the Performance of a Computing System with Deep Networks. In ICPE '23: Proceedings of the 2023 ACM/SPEC International Conference on Performance Engineering (91-98). https://doi.org/10.1145/3578244.3583731

• Conference Name: 2023 ACM/SPEC International Conference on Performance Engineering (ICPE ’23)
• Conference Location: Coimbra, Portugal
• Start Date: Apr 15, 2023
• End Date: Apr 19, 2023
• Acceptance Date: Dec 29, 2022
• Publication Date: 2023-04
• Deposit Date: Feb 27, 2023
• Publicly Available Date: Jul 27, 2023
• Pages: 91-98
• Book Title: ICPE '23: Proceedings of the 2023 ACM/SPEC International Conference on Performance Engineering
• DOI: https://doi.org/10.1145/3578244.3583731
• Public URL: https://durham-repository.worktribe.com/output/1134361