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GPU Computing with CUDA C++: A Practical Guide to GPU Architecture, Parallel Algorithms, Kernel Development, Thread Hierarchies, and Device Memory Pro
Paperback
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ISBN13: 9798166303325
Publisher: Independently Published
Published: Oct 29 2026
Pages: 310
Weight: 1.19
Height: 0.65 Width: 7.00 Depth: 10.00
Language: English
Publisher: Independently Published
Published: Oct 29 2026
Pages: 310
Weight: 1.19
Height: 0.65 Width: 7.00 Depth: 10.00
Language: English
Modern computing increasingly relies on parallel processing to handle computationally demanding tasks in scientific computing, data processing, simulations, artificial intelligence, and engineering applications. GPU computing makes it possible to execute large numbers of operations concurrently, but taking advantage of this capability requires an understanding of GPU architecture, parallel algorithms, and the CUDA programming model.
GPU Computing with CUDA C++ provides a practical introduction to developing GPU-accelerated applications using CUDA C++. It explains the fundamental concepts behind GPU execution and guides readers through the process of designing, implementing, and validating CUDA programs. From understanding threads, blocks, and grids to writing kernels and managing device memory, this book builds the technical foundation needed to work confidently with parallel GPU applications.
Rather than focusing only on isolated code examples, the book explores how CUDA components work together within complete application workflows. Readers learn how to organize parallel workloads, transfer data between CPU and GPU memory, coordinate thread execution, and address common programming errors. Practical discussions of shared memory, synchronization, streams, and asynchronous execution provide additional insight into how GPU applications operate.
What You'll Learn
This book is suitable for C++ programmers exploring GPU acceleration, students learning parallel computing, software developers working with computational workloads, and technical readers interested in NVIDIA CUDA development. Familiarity with basic C++ programming is helpful, although the book introduces the principal CUDA concepts progressively.
By the end of this book, readers will have a solid foundation for writing, understanding, and integrating CUDA C++ programs. It also prepares them to explore more advanced topics in kernel optimization, performance profiling, and GPU performance engineering.
Build your understanding of parallel computing and learn how to turn computational ideas into practical GPU-accelerated applications with CUDA C++.
Rather than focusing only on isolated code examples, the book explores how CUDA components work together within complete application workflows. Readers learn how to organize parallel workloads, transfer data between CPU and GPU memory, coordinate thread execution, and address common programming errors. Practical discussions of shared memory, synchronization, streams, and asynchronous execution provide additional insight into how GPU applications operate.
What You'll Learn
- Understand GPU architecture, parallel computing principles, and the CUDA programming model.
- Set up a CUDA C++ development environment and compile GPU-accelerated programs.
- Write CUDA kernels and organize threads, blocks, and grids for different computational workloads.
- Manage device memory, transfer data efficiently, and understand the GPU memory hierarchy.
- Use global memory, shared memory, registers, and other memory spaces appropriately.
- Implement practical parallel operations, including vector computations, matrix operations, and reductions.
- Apply thread synchronization and atomic operations to coordinate parallel execution safely.
- Work with CUDA streams and asynchronous operations to structure application workflows.
- Debug CUDA programs, validate numerical results, and identify common implementation errors.
- Integrate CUDA kernels into C++ applications and develop reusable GPU computing solutions.
This book is suitable for C++ programmers exploring GPU acceleration, students learning parallel computing, software developers working with computational workloads, and technical readers interested in NVIDIA CUDA development. Familiarity with basic C++ programming is helpful, although the book introduces the principal CUDA concepts progressively.
By the end of this book, readers will have a solid foundation for writing, understanding, and integrating CUDA C++ programs. It also prepares them to explore more advanced topics in kernel optimization, performance profiling, and GPU performance engineering.
Build your understanding of parallel computing and learn how to turn computational ideas into practical GPU-accelerated applications with CUDA C++.
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