This book presents a comprehensive exploration of GPGPU (General Purpose Graphics Processing Unit) and its applications in deep learning and machine learning. It focuses on how parallel computing, particularly through the use of CUDA (Compute Unified Device Architecture), can unlock unprecedented computational power for complex tasks. The book provides detailed discussions on CPU and GPU architectures, data flow in deep learning, and advanced GPU features like streams, concurrency, and dynamic parallelism. Furthermore, it delves into practical applications of GPGPU in various domains such as scientific computing, machine learning acceleration, real-time rendering, and cryptocurrency mining. The authors also emphasize the importance of selecting the right parallel architecture (e.g., GPU, FPGA, TPU, ASIC) based on specific tasks, offering insights into optimizing algorithms for these platforms. The book also provides practical examples with popular machine learning frameworks like PyTorch, TensorFlow, and XGBoost, demonstrating how to efficiently leverage GPU resources in both training and inference. This resource is valuable for both beginners and advanced readers who are looking to deepen their understanding of GPU-based parallel computing and its significant role in modern machine learning and AI applications.

翻译：本书全面探讨了GPGPU（通用图形处理器）及其在深度学习和机器学习中的应用。重点阐述了如何通过并行计算，特别是利用CUDA（统一计算设备架构），为复杂任务释放前所未有的计算能力。书中详细讨论了CPU与GPU架构、深度学习中的数据流，以及流、并发性和动态并行性等高级GPU特性。此外，本书深入研究了GPGPU在科学计算、机器学习加速、实时渲染和加密货币挖矿等多个领域的实际应用。作者还强调了根据具体任务选择合适并行架构（如GPU、FPGA、TPU、ASIC）的重要性，并提供了针对这些平台优化算法的见解。本书还通过PyTorch、TensorFlow和XGBoost等流行机器学习框架的实用示例，展示了如何在训练和推理中高效利用GPU资源。这一资源对于希望深入理解基于GPU的并行计算及其在现代机器学习和人工智能应用中重要作用的初学者和高级读者都具有重要价值。