Comparative Analysis of CPU and GPU Profiling for Deep Learning Models

TL;DR

This paper compares CPU and GPU performance in training deep neural networks, showing GPUs generally have lower training time than CPUs, especially for complex models, using PyTorch framework analysis.

Contribution

It provides a detailed comparison of CPU and GPU resource usage during deep learning training, highlighting performance differences with PyTorch.

Findings

GPU training is faster than CPU for deep neural networks.

For simple networks, GPU and CPU performance differences are minimal.

GPU offers significant time savings for complex models.

Abstract

Deep Learning(DL) and Machine Learning(ML) applications are rapidly increasing in recent days. Massive amounts of data are being generated over the internet which can derive meaningful results by the use of ML and DL algorithms. Hardware resources and open-source libraries have made it easy to implement these algorithms. Tensorflow and Pytorch are one of the leading frameworks for implementing ML projects. By using those frameworks, we can trace the operations executed on both GPU and CPU to analyze the resource allocations and consumption. This paper presents the time and memory allocation of CPU and GPU while training deep neural networks using Pytorch. This paper analysis shows that GPU has a lower running time as compared to CPU for deep neural networks. For a simpler network, there are not many significant improvements in GPU over the CPU.