Comparison study of GPU and CPU for deep learning methods
Charulatha.K
, Rajendiran.M , AYAPPAN.J , HEMANTHKUMAR.S , M.PADMARASAN
Applications for deep learning and machine learning have grown significantly in recent years. The application of ML and DL algorithms can produce relevant results from the vast quantity of information being generated online. It has been simple for us to implement these algorithms due to hardware resources and opensource libraries. One of the most popular frameworks for implementing ML projects is Tensorflow with PyTorch. With the help of such frameworks, we can track the operations that are carried out on the CPU and GPU to examine resource allocations and use. The time and memory allocation of the CPU and GPU during Pytorch-based deep neural network training is discussed in this research. The analysis of this research demonstrates that the GPU runs deep neural networks faster than the CPU. Few are the notable GPU gains over CPU for simpler networks.
"Comparison study of GPU and CPU for deep learning methods", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.11, Issue 2, page no.965-970, February-2024, Available :https://tijer.org/TIJER/papers/TIJERB001155.pdf
Volume 11
Issue 2,
February-2024
Pages : 965-970
Paper Reg. ID: TIJER_151102
Published Paper Id: TIJERB001155
Downloads: 000279
Research Area: Science and Technology
Country: Thiruvallur,Chennai, Tamil Nadu, India
ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: TIJER (IJ Publication) Janvi Wave
