Deep Learning and Machine Learning with GPGPU and CUDA: Unlocking the Power of Parallel Computing
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915626211082240 |
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| author | Li, Ming Bi, Ziqian Wang, Tianyang Wen, Yizhu Niu, Qian Song, Xinyuan Jiang, Zekun Liu, Junyu Peng, Benji Zhang, Sen Pan, Xuanhe Xu, Jiawei Wang, Jinlang Chen, Keyu Yin, Caitlyn Heqi Feng, Pohsun Liu, Ming |
| author_facet | Li, Ming Bi, Ziqian Wang, Tianyang Wen, Yizhu Niu, Qian Song, Xinyuan Jiang, Zekun Liu, Junyu Peng, Benji Zhang, Sen Pan, Xuanhe Xu, Jiawei Wang, Jinlang Chen, Keyu Yin, Caitlyn Heqi Feng, Pohsun Liu, Ming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05686 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Learning and Machine Learning with GPGPU and CUDA: Unlocking the Power of Parallel Computing Li, Ming Bi, Ziqian Wang, Tianyang Wen, Yizhu Niu, Qian Song, Xinyuan Jiang, Zekun Liu, Junyu Peng, Benji Zhang, Sen Pan, Xuanhe Xu, Jiawei Wang, Jinlang Chen, Keyu Yin, Caitlyn Heqi Feng, Pohsun Liu, Ming Distributed, Parallel, and Cluster Computing Hardware Architecture 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. |
| title | Deep Learning and Machine Learning with GPGPU and CUDA: Unlocking the Power of Parallel Computing |
| topic | Distributed, Parallel, and Cluster Computing Hardware Architecture |
| url | https://arxiv.org/abs/2410.05686 |