Predicting physical computer systems performance and power from simulation systems using machine learning model-Reference-Cited by-同舟云学术

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Predicting physical computer systems performance and power from simulation systems using machine learning model

Published:2022-03-15 Issue:5 Volume:105 Page:935-953
ISSN:0010-485X
Container-title:Computing
language:en
Short-container-title:Computing

Author:

Mankodi Amit^ORCID,Bhatt Amit,Chaudhury Bhaskar

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Numerical Analysis,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s00607-022-01066-5.pdf

Reference31 articles.

1. Sifakis J (2011) A vision for computer science-the system perspective. https://doi.org/10.2478/s13537-011-0008-y. http://www.mathworks.com/products/simulink/. Accessed 23 Aug 2020

2. Ranganathan P (2010) Recipe for efficiency: principles of power-aware computing. Commun ACM 53(4):60. https://doi.org/10.1145/1721654.1721673

3. Aldossary M, Djemame K, Alzamil I, Kostopoulos A, Dimakis A, Agiatzidou E (2019) Energy-aware cost prediction and pricing of virtual machines in cloud computing environments. Future Gener Comput Syst 93:442. https://doi.org/10.1016/j.future.2018.10.027

4. Ma J, Yan G, Han Y, Li X (2016) An analytical framework for estimating scale-out and scale-up power efficiency of heterogeneous Manycores. IEEE Trans Comput 65(2):367. https://doi.org/10.1109/TC.2015.2419655

5. Lai Z, Lam KT, Wang CL, Su J (2017) PoweRock: power modeling and flexible dynamic power management for many-core architectures. IEEE Syst J 11(2):600. https://doi.org/10.1109/JSYST.2015.2499307

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