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Deep Learning(DL) and Machine Learning(ML) applications are rapidlyincreasing in recent days. Massive amounts of data are being generated over theinternet which can derive meaningful results by the use of ML and DLalgorithms. Hardware resources and open-source libraries have made it easy toimplement these algorithms. Tensorflow and Pytorch are one of the leadingframeworks for implementing ML projects. By using those frameworks, we cantrace the operations executed on both GPU and CPU to analyze the resourceallocations and consumption. This paper presents the time and memory allocationof CPU and GPU while training deep neural networks using Pytorch. This paperanalysis shows that GPU has a lower running time as compared to CPU for deepneural networks. For a simpler network, there are not many significantimprovements in GPU over the CPU.

Comparative Analysis of CPU and GPU Profiling for Deep Learning Models