Deep Learning and Machine Learning with GPGPU and CUDA: Unlocking the Power of Parallel Computing
Ming Li, Ziqian Bi, Tianyang Wang, Yizhu Wen, Qian Niu, Xinyuan Song, Zekun Jiang, Junyu Liu, Benji Peng, Sen Zhang, Xuanhe Pan, Jiawei Xu, Jinlang Wang, Keyu Chen, Caitlyn Heqi Yin, Pohsun Feng, Ming Liu
TL;DR
This work surveys Deep Learning and Machine Learning with GPGPU and CUDA, detailing CPU/GPU architectures, memory hierarchies, and the data flow in training and inference. It comprehensively covers CUDA programming, GPU memory management, and optimization techniques (streams, dynamic parallelism, warp divergence, coalesced memory), alongside comparisons with alternative architectures (FPGA, TPU, ASIC) and cross-platform models (OpenCL, Vulkan, Metal, OpenGL). The document then highlights practical GPU libraries (cuBLAS, cuDNN, TensorRT, PyTorch, TensorFlow), applications across ML and scientific computing, GPU virtualization and cloud deployment, and future trends like AI-driven GPU growth, CPU-GPU hybrids, and potential quantum collaborations. By combining theoretical foundations with hands-on examples and performance strategies, it presents a complete, practitioner-oriented guide to leveraging GPUs for scalable ML and AI workloads.
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.