GPU Acceleration on Embedded Devices. A Power Consumption Approach

摘要

This paper analyses the power consumption of hybrid computation on embedded architectures with an available GPU. Novel efficiency metrics are obtained using a well-known benchmark process based on the Fourier transform as computing work load. The measurement process is arranged in order to obtain specific power data for each hardware configuration, varying the data size and number of computation threads, disabling the GPU, mixing the power computation of CPU/GPU, etc. The resulting data may be of interest for new applications and cluster development (i.e. Beowulf clusters) based on low power devices, such as the Beobot project.