Efficiency for data parallel computation in deep neural networks
Feng Yujun · Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022) · 2023
The data parallelism provides greater efficiency under multiple-node systems. Under these circumstances, there is more and more utilisation on multiple GPUs for better efficiency for computation and lower cost of time for accomplishing the project. However, some typical, ordinary, and common circumstances in which using multiple GPUs is worse than using one GPU in a machine. This paper aims to prove that using various GPUs is not omnipotent in efficiency and cost of time when running a program. This paper represents the limitations of computation with CPU, GPU, and multiple GPUs. Two main parts represent the limitations of the experiment. They are the comparison between one running CPU and one running GPU and the comparison between one running GPU and running multiple GPUs with the cost of time after running a program by numerous times and different data sizes