Advertisement

Soft Computing

, Volume 23, Issue 3, pp 827–836 | Cite as

A statistic approach for power analysis of integrated GPU

  • Qiong WangEmail author
  • Ning Li
  • Li Shen
  • Zhiying Wang
Methodologies and Application
  • 94 Downloads

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.

Keywords

Integrated GPU Power analysis Statistical model Kernel extension 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (61472431, 61272143 and 61272144).

Compliance with ethical standards

Conflicts of interest

All authors declare that they have no conflicts of interest regarding the publication of this manuscript.

References

  1. 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 105–114Google Scholar
  2. Branover A, Foley D, Steinman M (2012) Amd fusion apu: Llano. IEEE Micro 32(2):28–37CrossRefGoogle Scholar
  3. 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):1370–1380CrossRefGoogle Scholar
  4. Chitty DM (2016) Improving the performance of gpu-based genetic programming through exploitation of on-chip memory. Soft Comput 20(2):661–680CrossRefGoogle Scholar
  5. Corparation I (2016a) Intel core i7-920 processor. http://ark.intel.com/product.aspx?id=37147
  6. Corparation N (2016b) Geforce gtx 280. http://www.nvidia.com/object/product_geforce_gtx280_us.html
  7. Corparation N (2016c) What is cuda. http://www.nvidia.com/object/what_is_cuda_new.html
  8. Corparation N (2017) Machine learning. http://www.nvidia.com/object/machine-learning.html
  9. Diop T, Jerger NE, Anderson J (2014) Power modeling for heterogeneous processors. In: Proceedings of workshop on general purpose processing using GPUs, p 90Google Scholar
  10. 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 152–163Google Scholar
  11. Karami A, Khunjush F, Mirsoleimani SA (2015) A statistical performance analyzer framework for opencl kernels on nvidia gpus. J Supercomput 71(8):2900–2921Google Scholar
  12. 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 15–22Google Scholar
  13. 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 487–498Google Scholar
  14. Li J, Du Q, Li Y (2016) An efficient radial basis function neural network for hyperspectral remote sensing image classification. Soft Comput 20(12):4753–4759CrossRefGoogle Scholar
  15. 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 658–665Google Scholar
  16. Stone JE, Gohara D, Shi G (2010) Opencl: a parallel programming standard for heterogeneous computing systems. Comput Sci Eng 12(3):66–73CrossRefGoogle Scholar
  17. 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 300–303Google Scholar
  18. 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 564–576Google Scholar
  19. 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 382–393Google Scholar
  20. Zhang H, Xiao N (2016) Parallel implementation of multilayered neural networks based on map-reduce on cloud computing clusters. Soft Comput 20(4):1471–1483MathSciNetCrossRefGoogle Scholar
  21. 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 149–158Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.College of ComputerNational University of Defense TechnologyChangshaChina

Personalised recommendations