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GPU Programming with CUDA For Beginners: A Practical Guide to CUDA Programming, GPU Kernels, Memory Management, and Parallel Computing
Paperback
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ISBN13: 9798171905576
Publisher: Independently Published
Published: Sep 4 2026
Pages: 212
Weight: 0.83
Height: 0.45 Width: 7.00 Depth: 10.00
Language: English
Publisher: Independently Published
Published: Sep 4 2026
Pages: 212
Weight: 0.83
Height: 0.45 Width: 7.00 Depth: 10.00
Language: English
GPU's have become an important part of modern computing, enabling applications to process large amounts of data through massive parallelism. CUDA provides developers with a powerful programming model for taking advantage of NVIDIA GPUs, but getting started can be challenging when concepts such as kernels, threads, blocks, grids, memory spaces, and synchronization are introduced all at once.
GPU Programming with CUDA For Beginners provides a practical introduction to CUDA programming for readers who want to understand how GPU computing works and learn how to build CUDA applications from the ground up.
Starting with the fundamentals of GPU architecture and parallel computing, the book progressively introduces the CUDA programming model, kernel execution, threads, thread blocks, grids, memory management, data transfer, synchronization, and common parallel programming patterns. Practical examples help connect these concepts to the way CUDA programs actually execute on a GPU.
You will explore how to:
Whether you are a programmer new to GPU computing, a software developer expanding into parallel programming, or a technical professional looking to understand CUDA, this book provides a structured starting point for building useful GPU programming skills.
Build the fundamentals first. Then use them to write better GPU programs.
Starting with the fundamentals of GPU architecture and parallel computing, the book progressively introduces the CUDA programming model, kernel execution, threads, thread blocks, grids, memory management, data transfer, synchronization, and common parallel programming patterns. Practical examples help connect these concepts to the way CUDA programs actually execute on a GPU.
You will explore how to:
- Understand the fundamentals of GPU computing and parallel execution
- Set up a CUDA development environment
- Write and launch CUDA kernels
- Work with threads, blocks, grids, and execution hierarchies
- Manage data between the CPU and GPU
- Understand CUDA's different memory spaces
- Use synchronization and coordinate parallel work
- Design practical parallel algorithms
- Identify common CUDA programming mistakes
- Structure CUDA applications more effectively
- Build a solid foundation for further CUDA development and optimization
Whether you are a programmer new to GPU computing, a software developer expanding into parallel programming, or a technical professional looking to understand CUDA, this book provides a structured starting point for building useful GPU programming skills.
Build the fundamentals first. Then use them to write better GPU programs.
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