Comparative analysis of the efficiency of using gpus in machine learning

In today’s world, data processing and machine learning play a key role in various sectors, including science, industry and technology. As the volume of data increases, the need for effective methods and tools for their analysis increases. Graphics processing units (GPUs) become a necessary element in this process, as they allow complex calculations to be performed in parallel. This article analyzes the efficiency of GPU usage in machine learning. The research covers the use of the RAPIDS libraries (cuMl and cuDF) to perform data analysis and implement machine learning algorithms on the GPU. Alternative machine learning methods, including linear regression and SVM, implemented using the RAPIDS and Scikit-Learn libraries, are also being studied. The presented analysis highlights the advantages of using GPU in training models, especially when working with large amounts of data. The algorithms considered in the study were tested on a dataset created using the Buckley-Leverett mathematical model to predict the oil recovery coefficient. The article demonstrates the importance and potential of using GPUs in machine learning, highlighting key methods that contribute to improving the performance and efficiency of algorithms.