Performance Analysis and CPU vs GPU Comparison for Deep Learning

Abstract

Deep learning approaches are machine learning methods used in many application fields today. Some core mathematical operations performed in deep learning are suitable to be parallelized. Parallel processing increases the operating speed. Graphical Processing Units (GPU) are used frequently for parallel processing. Parallelization capacities of GPUs are higher than CPUs, because GPUs have far more cores than Central Processing Units (CPUs). In this study, benchmarking tests were performed between CPU and GPU. Tesla k80 GPU and Intel Xeon Gold 6126 CPU was used during tests. A system for classifying Web pages with Recurrent Neural Network (RNN) architecture was used to compare performance during testing. CPUs and GPUs running on the cloud were used in the tests because the amount of hardware needed for the tests was high. During the tests, some hyperparameters were adjusted and the performance values were compared between CPU and GPU. It has been observed that the GPU runs faster than the CPU in all tests performed. In some cases, GPU is 4-5 times faster than CPU, according to the tests performed on GPU server and CPU server. These values can be further increased by using a GPU server with more features.