The CUDA Handbook: A Comprehensive Guide to GPU Programming (2nd Edition)

The CUDA Handbook is the only comprehensive reference to CUDA that exists. Every CUDA developer, from the casual to the most sophisticated, will find something here of interest and immediate usefulness. Newer CUDA developers will see how the hardware processes commands and how the driver checks progress; more experienced CUDA developers will appreciate the expert coverage of topics such as the driver API and context migration, as well as the guidance on how best to structure CPU/GPU data interchange and synchronization.

The accompanying open source code—more than 30,000 lines of it, freely available from github — is specifically intended to be reused and repurposed by developers.

• GPU Parallel Program Development Using CUDA (Chapman & Hall/CRC Computational Science)
• Programming Massively Parallel Processors: A Hands-on Approach
• CUDA for Engineers: An Introduction to High-Performance Parallel Computing
• AWS DeepRacer – Fully autonomous 1/18th scale race car for developers
• Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more
• Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series)
• Windows Internals, Part 1: System architecture, processes, threads, memory management, and more (7th Edition)
• Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more
• Pattern Recognition and Machine Learning (Information Science and Statistics)
• CUDA by Example: An Introduction to General-Purpose GPU Programming