Hands-On GPU programming with CUDA

• Understand general GPU operations and programming patterns in CUDA
• Uncover the difference between GPU programming and CPU programming
• Analyze GPU application performance and implement optimization strategies
• Explore GPU programming, profiling, and debugging tools
• Grasp parallel programming algorithms and their implementations
• Scale GPU-accelerated applications with multi-GPU and multi-nodes
• Delve into GPU programming platforms with accelerated libraries, Python, and OpenACC
• Gain insights into deep learning accelerators in CNNs and RNNs using GPU

Compute Unified Device Architecture (CUDA) is NVIDIA's GPU computing platform and application programming interface. It’s designed to work with programming languages such as C, C++, and Python. With CUDA, you can leverage a GPU's parallel computing power for a range of high-performance computing applications in the fields of science, healthcare, and deep learning.

Hands-On GPU Programming with CUDA will help you learn GPU parallel programming and understand its modern applications. With this book, you'll discover CUDA programming approaches for modern GPU architectures. You’ll not only be guided through GPU features, tools, and APIs, but also be able to analyze performance with sample parallel programming algorithms. This book will help you optimize the performance of your apps by giving insights into CUDA programming platforms with various libraries, compiler directives (OpenACC), and other languages. As you progress, you'll how additional computing power can be generated using multiple GPUs in a box or in multiple boxes. Finally, you'll explore how CUDA accelerates deep learning algorithms, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

By the end of this CUDA book, you’ll be equipped with the skills you need to integrate the power of GPU computing in your applications.

• Learn parallel programming principles and practices and performance analysis in GPU computing
• Get to grips with distributed multi GPU programming and other approaches to GPU programming
• Understand how GPU acceleration in deep learning models can improve their performance