High-Performance GPU Programming with C++ and Cuda: Master CUDA C++ to Build Blazing-Fast Parallel Applications, from Core Algorithms to AI-Driven Agentic RAG

by Ryan Knight

High-Performance GPU Programming with C++ and CUDA The future of speed isn’t in faster CPUs, it’s in parallelism. Unlock the true power of your GPU and learn how to turn your C++ code into lightning-fast, high-performance applications that scale across thousands of cores.

What This Book Allows You to DoThis book empowers you to transform your C++ programs into GPU-accelerated powerhouses using NVIDIA’s CUDA platform. You’ll learn to identify performance bottlenecks, write efficient kernels, leverage shared memory, and even integrate AI-driven optimization to build applications that run faster and smarter.

About the TechnologyCUDA C++ brings high-performance computing to your fingertips. It enables developers to offload computationally heavy tasks from the CPU to the GPU, executing thousands of parallel threads simultaneously. With NVIDIA’s CUDA Toolkit, Nsight profiler, and cuBLAS libraries, you’ll gain mastery over real-world optimization and profiling workflows used in professional GPU programming environments.

Book SummaryHigh-Performance GPU Programming with C++ and CUDA bridges the gap between theory and practice in parallel computing. Designed for modern C++ developers, it provides a hands-on, structured path to mastering GPU acceleration. You’ll begin by learning the fundamentals, memory management, thread hierarchies, and kernel launches, before progressing to complex optimizations such as shared memory tiling, warp divergence handling, and asynchronous streams.

In later chapters, the book explores advanced topics like AI-assisted optimization workflows using Agentic RAG (Retrieval-Augmented Generation), illustrating how artificial intelligence can analyze, profile, and refactor GPU code automatically. Each chapter follows the powerful “Profile → Optimize → Repeat” workflow, ensuring every concept is grounded in measurable performance gains.

What’s Inside This BookA complete workflow for writing, profiling, and optimizing CUDA C++ programs

Step-by-step examples of kernels, grids, blocks, and threads

Hands-on guidance for memory optimization using shared and global memory

Real-world reduction, matrix multiplication, and Monte Carlo simulation

Modern C++ techniques for safe GPU memory management (smart pointers, lambdas)

Debugging and profiling strategies with Nsight Compute and cuda-gdb

AI-driven performance tuning using agentic code optimization loops

About the ReaderThis book is written for C++ developers, data scientists, AI engineers, game developers, and HPC researchers who are ready to elevate their performance engineering skills. Whether you’re building simulations, financial models, or neural network accelerators, this guide helps you bridge the gap between traditional CPU programming and the massively parallel world of GPU computing.

If you’re ready to go beyond theory and start building real, measurable, high-performance GPU applications, this is your essential roadmap.

317 pages, Kindle Edition

Published October 29, 2025