As CPUs reach their limits and accelerators take over, mastering GPU programming is no longer optional, it’s essential. High Performance GPU Computing with C++ and CUDA is your complete, practical roadmap to building blazing-fast, hardware-agnostic applications using the tools that power today’s most advanced computing systems. What This Book Allows You to Do This book teaches you how to think, design, and code like a modern GPU engineer . You’ll learn how to build scalable CUDA applications, optimize memory usage, understand GPU architecture, eliminate performance bottlenecks, and write clean, robust C++20/C++23 code that runs efficiently across hardware generations. About the Technology CUDA, C++, and modern accelerators form the backbone of scientific computing, AI/ML pipelines, real-time graphics, and high-throughput data processing. This book demystifies the execution model, thread scheduling, memory hierarchy, asynchrony, concurrency, and profiling so you can exploit the GPU’s massive parallelism with confidence. Book Summary High Performance GPU Computing with C++ and CUDA takes you from the foundational principles of heterogeneous computing all the way to advanced, production-grade pipelines. You’ll start by understanding why Moore’s Law ended and how accelerators transformed the landscape of modern software engineering. From there, the book builds your skills step-by-step, covering kernels, memory models, SM architecture, warps, coalescing, occupancy, debugging, and libraries such as Thrust, CUB, cuBLAS, and NCCL. Across its chapters, the book moves from theory to hands-on implementation, culminating in a real-time GPU image-processing pipeline and multi-GPU scaling frameworks. With clear examples, deep insights, and a hardware-agnostic philosophy, this book prepares you to write high-performance code that stands the test of evolving GPU architectures. What’s Inside This Book? (5–7 Key Benefits) Master the CUDA execution model, warps, SIMT, independent thread scheduling, launch bounds, occupancy tuning, and register pressure. - Write clean, future-proof C++20/C++23 GPU code using modern templates, lambdas, and standard parallelism (std::par). - Learn memory systems like an engineer, Unified Memory, Pinned Memory, coalescing, shared memory tiling, constant/texture memory, async copies, and bandwidth profiling. - Use professional-grade tools —CMake, Nsight Systems, Nsight Compute, NVTX markers, Compute Sanitizer, and unit testing. - Build real-world GPU pipelines including image processing, fluid simulations, concurrency with streams, and CUDA Graphs. - Scale to multiple GPUs with NCCL, P2P access, unified virtual addressing, and adaptive workload splitting. - Develop hardware-agnostic thinking to write code that runs efficiently across GPU generations and platforms. About the Reader This book is perfect for: Students learning CUDA or parallel programming for the first time - Software engineers building GPU-accelerated applications - Researchers running simulations, HPC workloads, or AI pipelines - C++ developers who want to exploit massive parallelism cleanly and safely - Anyone who wants a practical, modern, engineering-grade guide to CUDA If you’re ready to take your C++ skills into the world of real parallel computing, and unlock performance that CPUs simply cannot reach, then scroll up and get your copy of High Performance GPU Computing with C++ and CUDA today. Your journey to mastering GPU acceleration begins now.