GPU Energy Consumption Optimization With a Global-Based Neural Network Method

With the widespread use of smart technologies, graphics processing unit (GPU) power-optimization issues are becoming increasingly important. Many researchers have tried to use dynamic voltage and frequency scaling (DVFS) technology to optimize a GPU's internal energy consumption. However, DVFS energy management often has difficulty balancing GPU performance and energy efficiency. This paper aims to implement a DVFS energy management strategy. We constructed a new type of neural network to a GPU-based energy management scheme, implemented the global-based DVFS model, and explored its implementation details. Using a master-slave model, we built a global energy control solution strategy. This strategy performs global collaborative DVFS adjustments on the GPU's energy consumption module based on task characteristics. Through the software construction and implementation of the global-based DVFS model, we proved that the strategy improves the GPU performance while improving the GPU's energy efficiency. We conducted performance and energy tests on three GPUs on the Tesla, Fermi, and Kepler platforms. The experiments showed that this strategy improved the performance and power consumption of GPUs based on each of the platforms.