Deep Learning and Machine Learning with GPGPU and CUDA: Unlocking the Power of Parallel Computing
Abstract
General Purpose Graphics Processing Unit (GPGPU) computing plays a transformative role in deep learning and machine learning by leveraging the computational advantages of parallel processing. Through the power of Compute Unified Device Architecture (CUDA), GPUs enable the efficient execution of complex tasks via massive parallelism. This work explores CPU and GPU architectures, data flow in deep learning, and advanced GPU features, including streams, concurrency, and dynamic parallelism. The applications of GPGPU span scientific computing, machine learning acceleration, real-time rendering, and cryptocurrency mining. This study emphasizes the importance of selecting appropriate parallel architectures, such as GPUs, FPGAs, TPUs, and ASICs, tailored to specific computational tasks and optimizing algorithms for these platforms. Practical examples using popular frameworks such as PyTorch, TensorFlow, and XGBoost demonstrate how to maximize GPU efficiency for training and inference tasks. This resource serves as a comprehensive guide for both beginners and experienced practitioners, offering insights into GPU-based parallel computing and its critical role in advancing machine learning and artificial intelligence.
Cite
@article{arxiv.2410.05686,
title = {Deep Learning and Machine Learning with GPGPU and CUDA: Unlocking the Power of Parallel Computing},
author = {Ming Li and Ziqian Bi and Tianyang Wang and Yizhu Wen and Qian Niu and Xinyuan Song and Zekun Jiang and Junyu Liu and Benji Peng and Sen Zhang and Xuanhe Pan and Jiawei Xu and Jinlang Wang and Keyu Chen and Caitlyn Heqi Yin and Pohsun Feng and Ming Liu},
journal= {arXiv preprint arXiv:2410.05686},
year = {2025}
}
Comments
106 pages