Energy consumption estimation for 3D graphics rendering

被引：0
|
作者：
Xing L. [1 ,2 ]
Li T. [2 ]
Huang H. [2 ]
Han J. [2 ]
机构：
[1] School of Microelectronics, Xidian Univ., Xi'an
[2] School of Electronic Engineering, Xi'an Univ. of Posts and Telecommunications, Xi'an
来源：
Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2017年 / 44卷 / 04期
关键词：
3D graphics rendering; Energy consumption estimation model; Prediction error; Shader;
D O I：
10.3969/j.issn.1001-2400.2017.04.008
中图分类号：
学科分类号：
摘要：
From the perspective of hardware design and graphics programming, a new energy consumption estimation model of 3D graphics rendering is proposed. Aiming at the energy consumption of the vertex shader and pixel shader in the 3D rendering pipeline, the loads of the vertex shader and the pixel shader which affect the rendering quality are analyzed, and the model of the ratio the number of pixels to the number of vertexes is obtained. At the same time, the energy consumption of vertex shading stages and pixel shading stages are modeled. The model is used to estimate the energy consumption of the benchmark programs, and simulations are performed using the Synopsys VCS simulator and the Power Compiler to obtain the actual energy consumption of these programs. The results show that the geometric mean of the model's prediction error is less than 3%, which can be used to guide the power consumption analysis and management of graphics hardware and software. © 2017, The Editorial Board of Journal of Xidian University. All right reserved.
引用
收藏
页码：40 / 46and74
页数：4634
相关论文
共 13 条
  • [1] Collange S., Defour D., Tisserand A., Power Consumption of GPUs from a Software Perspective, Lecture Notes in Computer Science, 5544, pp. 914-923, (2009)
  • [2] Shaikh M.Z., Gregoire M., Li W., Et al., In Situ Power Analysis of General Purpose Graphical Processing Units, Proceedings of the 19th International Euromicro Conference on Parallel, Distributed and Network, pp. 40-44, (2011)
  • [3] Johnsson B., Akenine-Moller T., Measuring Per-frame Energy Consumption of Real-time Graphics Applications, Journal of Computer Graphics Techniques, 3, 1, pp. 60-73, (2014)
  • [4] Wang H., Cao Y., Power Consumption Optimization Control Model of GPU Clusters, Acta Electronica Sinica, 43, 10, pp. 1904-1910, (2015)
  • [5] Rakvic R., Broussard R., Ngo H., Energy Efficient Iris Recognition with Graphics Processing Units, IEEE Access, 4, pp. 2831-2839, (2016)
  • [6] Wu G., Greathouse J.L., Lyashevsky A., Et al., GPGPU Performance and Power Estimation Using Machine Learning, Proceedings of the 2015 IEEE 21st International Symposium on High Performance Computer Architecture, pp. 564-576, (2015)
  • [7] Nagasaka H., Maruyama N., Nukada A., Et al., Statistical Power Modeling of GPU Kernels Using Performance Counters, Proceedings of the 2010 International Conference on Green Computing, pp. 115-122, (2010)
  • [8] Ma X., Dong M., Zhong L., Et al., Statistical Power Consumption Analysis and Modeling for GPU-based Computing, Proceedings of the Workshop on Power Aware Computing and Systems(HotPower'09), pp. 1-5, (2009)
  • [9] Wang H.F., Chen Q.K., Power Estimating Model and Analysis of General Programming on GPU, Journal of Software, 7, 5, pp. 1164-1170, (2012)
  • [10] Kasichayanula K., Terpstra D., Luszczek P., Et al., Power Aware Computing on GPUs, Proceedings of the Symposium on Application Accelerators in High-performance Computing, pp. 64-73, (2012)
12