Green Computing using GPU in Image Processing

Authors(1) :-Chahat Monga

Green computing is the process of reducing the power consumed by a computer and thereby reducing carbon emissions. The total power consumed by the computer excluding the monitor at its fully computative load is equal to the sum of the power consumed by the GPU in its idle state and the CPU at its full state. Recently, there have been tremendous interests in the acceleration of general computing applications using a Graphics Processing Unit (GPU). Now the GPU provides the computing powers not only for fast processing of graphics applications, but also for general computationally complex data intensive applications. On the other hand, power and energy consumptions are also becoming important design criteria. Consequently, software designs have to consider the power/energy consumptions together with performance when they are developing software.The GPU therefore does the 100% of the CPU work in its idle state .Hence the power consumed by the GPU will be low. Also when the GPU is doing all the work the CPU will remain at a load less than its idle load. Hence the power consumed will be equal to the power consumed by the CPU at a load less than its idle load plus the power consumed by a GPU.

Authors and Affiliations

Chahat Monga
Assistant Professor, Guru Nanak college, Department of Computer Science, Ferozepur, Punjab, India

GPU (Graphical Processing Unit), CUDA, OpenCV, nVidia, Image Processing.

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Publication Details

Published in : Volume 3 | Issue 1 | January-February 2018
Date of Publication : 2018-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 717-722
Manuscript Number : CSEIT1831214
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Chahat Monga, "Green Computing using GPU in Image Processing", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.717-722, January-February-2018.
Journal URL : http://ijsrcseit.com/CSEIT1831214

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