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DOI: Improving Time Efficiency of Machine Learning Algorithms Through GPU Parallelization
Fayzullo Fozilov , Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Uzbekistan Murodjon Abdusadikov , Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Uzbekistan Khurshid Turaev , Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Uzbekistan Nozima Atadjanova , Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Uzbekistan Indira Tursinkulova , Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, UzbekistanAbstract
This paper discusses the application of parallel computing technologies in artificial intelligence and machine learning processes. The study focuses on heterogeneous computing systems based on CPUs and GPUs, as well as the use of CUDA technology for parallel data processing. Experimental results show that GPU-based parallelization significantly improves computational speed and reduces execution time compared to traditional CPU-based processing. The research confirms the effectiveness of GPUs in accelerating machine learning algorithms and other computationally intensive tasks.
Keywords
Parallel processing algorithms, artificial intelligence, machine learning
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Copyright (c) 2026 Fayzullo Fozilov, Murodjon Abdusadikov, Khurshid Turaev, Nozima Atadjanova, Indira Tursinkulova
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