CUDA Programming: A Developer's Guide to Parallel Computing with GPUs (Applications of Gpu Computing)

If you need to learn CUDA but don't have experience with parallel computing, CUDA Programming: A Developer's Introduction offers a detailed guide to CUDA with a grounding in parallel fundamentals. It starts by introducing CUDA and bringing you up to speed on GPU parallelism and hardware, then delving into CUDA installation. Chapters on core concepts including threads, blocks, grids, and memory focus on both parallel and CUDA-specific issues. Later, the book demonstrates CUDA in practice for optimizing applications, adjusting to new hardware, and solving common problems.

• Comprehensive introduction to parallel programming with CUDA, for readers new to both
• Detailed instructions help readers optimize the CUDA software development kit
• Practical techniques illustrate working with memory, threads, algorithms, resources, and more
• Covers CUDA on multiple hardware platforms: Mac, Linux and Windows with several NVIDIA chipsets
• Each chapter includes exercises to test reader knowledge

• CUDA by Example: An Introduction to General-Purpose GPU Programming
• CUDA for Engineers: An Introduction to High-Performance Parallel Computing
• Professional CUDA C Programming
• Programming Massively Parallel Processors: A Hands-on Approach
• CUDA Handbook: A Comprehensive Guide to GPU Programming, The
• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
• Cloud Computing Networking: Theory, Practice, and Development
• Multicore and GPU Programming: An Integrated Approach
• OpenCL in Action: How to Accelerate Graphics and Computations
• TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning