Skip to main content
Erschienen in: Soft Computing 3/2019

17.08.2017 | Methodologies and Application

A statistic approach for power analysis of integrated GPU

verfasst von: Qiong Wang, Ning Li, Li Shen, Zhiying Wang

Erschienen in: Soft Computing | Ausgabe 3/2019

Einloggen

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

As datasets grow, high performance computing has gradually become an important tool for artificial intelligence, particularly due to the powerful and efficient parallel computing provided by GPUs. However, it has been a general concern that the rising performance of GPUs usually consumes high power. In this work, we investigate the study of evaluating the power consumption of AMD’s integrated GPU (iGPU). Particularly, by adopting the linear regression method on the collecting data of performance counters, we model the power of iGPU using real hardware measurements. Unfortunately, the profiling tool CodeXL cannot be straightforwardly used for sampling power data and as a countermeasure we propose a mechanism called kernel extension to enable the system data sampling for model evaluation. Experimental results indicate that the median absolute error of our model is less than 3%. Furthermore, we simplify our statistical model for lower latency without significantly reducing the accuracy and stability.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literatur
Zurück zum Zitat Baghsorkhi SS, Delahaye M, Patel SJ, Gropp WD, Hwu WMW (2010) An adaptive performance modeling tool for gpu architectures. In: ACM sigplan notices, vol 45, pp 105–114 Baghsorkhi SS, Delahaye M, Patel SJ, Gropp WD, Hwu WMW (2010) An adaptive performance modeling tool for gpu architectures. In: ACM sigplan notices, vol 45, pp 105–114
Zurück zum Zitat Branover A, Foley D, Steinman M (2012) Amd fusion apu: Llano. IEEE Micro 32(2):28–37CrossRef Branover A, Foley D, Steinman M (2012) Amd fusion apu: Llano. IEEE Micro 32(2):28–37CrossRef
Zurück zum Zitat Che S, Boyer M, Meng J, Tarjan D, Sheaffer JW, Skadron K (2008) A performance study of general-purpose applications on graphics processors using cuda. J Parallel Distrib Comput 68(10):1370–1380CrossRef Che S, Boyer M, Meng J, Tarjan D, Sheaffer JW, Skadron K (2008) A performance study of general-purpose applications on graphics processors using cuda. J Parallel Distrib Comput 68(10):1370–1380CrossRef
Zurück zum Zitat Chitty DM (2016) Improving the performance of gpu-based genetic programming through exploitation of on-chip memory. Soft Comput 20(2):661–680CrossRef Chitty DM (2016) Improving the performance of gpu-based genetic programming through exploitation of on-chip memory. Soft Comput 20(2):661–680CrossRef
Zurück zum Zitat Diop T, Jerger NE, Anderson J (2014) Power modeling for heterogeneous processors. In: Proceedings of workshop on general purpose processing using GPUs, p 90 Diop T, Jerger NE, Anderson J (2014) Power modeling for heterogeneous processors. In: Proceedings of workshop on general purpose processing using GPUs, p 90
Zurück zum Zitat Hong S, Kim H (2009) An analytical model for a gpu architecture with memory-level and thread-level parallelism awareness. In: ACM SIGARCH computer architecture news, vol 37, pp 152–163 Hong S, Kim H (2009) An analytical model for a gpu architecture with memory-level and thread-level parallelism awareness. In: ACM SIGARCH computer architecture news, vol 37, pp 152–163
Zurück zum Zitat Karami A, Khunjush F, Mirsoleimani SA (2015) A statistical performance analyzer framework for opencl kernels on nvidia gpus. J Supercomput 71(8):2900–2921 Karami A, Khunjush F, Mirsoleimani SA (2015) A statistical performance analyzer framework for opencl kernels on nvidia gpus. J Supercomput 71(8):2900–2921
Zurück zum Zitat Karami A, Mirsoleimani SA, Khunjush F (2013) A statistical performance prediction model for opencl kernels on nvidia gpus. In: 2013 17th CSI international symposium on computer architecture and digital systems (CADS), pp 15–22 Karami A, Mirsoleimani SA, Khunjush F (2013) A statistical performance prediction model for opencl kernels on nvidia gpus. In: 2013 17th CSI international symposium on computer architecture and digital systems (CADS), pp 15–22
Zurück zum Zitat Leng J, Hetherington T, ElTantawy A, Gilani S, Kim NS, Aamodt TM, Reddi VJ (2013) Gpuwattch: enabling energy optimizations in gpgpus. In: ACM SIGARCH computer architecture news, vol 41, pp 487–498 Leng J, Hetherington T, ElTantawy A, Gilani S, Kim NS, Aamodt TM, Reddi VJ (2013) Gpuwattch: enabling energy optimizations in gpgpus. In: ACM SIGARCH computer architecture news, vol 41, pp 487–498
Zurück zum Zitat Li J, Du Q, Li Y (2016) An efficient radial basis function neural network for hyperspectral remote sensing image classification. Soft Comput 20(12):4753–4759CrossRef Li J, Du Q, Li Y (2016) An efficient radial basis function neural network for hyperspectral remote sensing image classification. Soft Comput 20(12):4753–4759CrossRef
Zurück zum Zitat Luo C, Suda R (2011) A performance and energy consumption analytical model for gpu. In: 2011 IEEE ninth international conference on dependable, autonomic and secure computing (DASC), pp 658–665 Luo C, Suda R (2011) A performance and energy consumption analytical model for gpu. In: 2011 IEEE ninth international conference on dependable, autonomic and secure computing (DASC), pp 658–665
Zurück zum Zitat Stone JE, Gohara D, Shi G (2010) Opencl: a parallel programming standard for heterogeneous computing systems. Comput Sci Eng 12(3):66–73CrossRef Stone JE, Gohara D, Shi G (2010) Opencl: a parallel programming standard for heterogeneous computing systems. Comput Sci Eng 12(3):66–73CrossRef
Zurück zum Zitat Wang Y, Roy S, Ranganathan N (2012) Run-time power-gating in caches of gpus for leakage energy savings. In: Design, automation & test in Europe conference & exhibition (DATE), 2012, pp 300–303 Wang Y, Roy S, Ranganathan N (2012) Run-time power-gating in caches of gpus for leakage energy savings. In: Design, automation & test in Europe conference & exhibition (DATE), 2012, pp 300–303
Zurück zum Zitat Wu G, Greathouse JL, Lyashevsky A, Jayasena N, Chiou D (2015) Gpgpu performance and power estimation using machine learning. In: 2015 IEEE 21st international symposium on high performance computer architecture (HPCA), pp 564–576 Wu G, Greathouse JL, Lyashevsky A, Jayasena N, Chiou D (2015) Gpgpu performance and power estimation using machine learning. In: 2015 IEEE 21st international symposium on high performance computer architecture (HPCA), pp 564–576
Zurück zum Zitat Zhang Y, Owens JD (2011) A quantitative performance analysis model for gpu architectures. In: 2011 IEEE 17th international symposium on high performance computer architecture (HPCA), pp 382–393 Zhang Y, Owens JD (2011) A quantitative performance analysis model for gpu architectures. In: 2011 IEEE 17th international symposium on high performance computer architecture (HPCA), pp 382–393
Zurück zum Zitat Zhang H, Xiao N (2016) Parallel implementation of multilayered neural networks based on map-reduce on cloud computing clusters. Soft Comput 20(4):1471–1483MathSciNetCrossRef Zhang H, Xiao N (2016) Parallel implementation of multilayered neural networks based on map-reduce on cloud computing clusters. Soft Comput 20(4):1471–1483MathSciNetCrossRef
Zurück zum Zitat Zhang Y, Hu Y, Li B, Peng L (2011) Performance and power analysis of ati gpu: a statistical approach. In: 2011 6th IEEE international conference on networking, architecture and storage (NAS), pp 149–158 Zhang Y, Hu Y, Li B, Peng L (2011) Performance and power analysis of ati gpu: a statistical approach. In: 2011 6th IEEE international conference on networking, architecture and storage (NAS), pp 149–158
Metadaten
Titel
A statistic approach for power analysis of integrated GPU
verfasst von
Qiong Wang
Ning Li
Li Shen
Zhiying Wang
Publikationsdatum
17.08.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 3/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-017-2786-1

Weitere Artikel der Ausgabe 3/2019

Soft Computing 3/2019 Zur Ausgabe