DVFS Algorithms of GPU and Memory for Mobile GPGPU Applications: A case study

  • Authors

    • SeongKi Kim Keimyung University
    • Seok-Kyoo Kim Sangmyung University
    2018-08-24
    https://doi.org/10.14419/ijet.v7i3.15296
  • DVFS, Embedded system, GPU, GPGPU, Mobile system, OpenCL

  • Although both OpenCL and RenderScript have allowed the General-Purpose Graphics Processing Unit (GPGPU) to be used even for mobile GPUs, it is still difficult for mobile applications to use the GPGPU for several reasons. One of the reasons is that mobile devices place restrictions on GPU performance through power-saving technologies such as Dynamic Voltage and Frequency Scaling (DVFS). DVFS tries to control the balance between performance and energy consumption based on the application’s requirements. This technology has been successful in many cases and is widely used; however, it significantly decreases the performance of GPGPU applications. In this paper, we propose novel DVFS algorithms for GPU and memory when the GPGPU applications run. The suggested algorithms decreased the energy consumption by more than 0.7 times without any algorithm changes, and improved the energy efficiency (performance per watt) by more than 3.42 times in comparison with the conventional interval-based algorithm.

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  • How to Cite

    Kim, S., & Kim, S.-K. (2018). DVFS Algorithms of GPU and Memory for Mobile GPGPU Applications: A case study. International Journal of Engineering & Technology, 7(3), 1918-1925. https://doi.org/10.14419/ijet.v7i3.15296