GPU Programming with C++ and CUDA
Uncover effective techniques for writing efficient GPU-parallel C++ applications
Created by Paulo Motta
Unlock the full potential of your C++ applications by learning how to leverage GPU programming with CUDA. Discover practical techniques for writing scalable parallel code, optimizing performance, and building GPU-accelerated libraries that integrate with Python workflows.
Packt | Aug 2025 | 270 min
What You Will Learn
You will start by exploring the basics of parallel thinking and GPU programming, then move on to designing parallel algorithms and optimizing performance. As you progress, you'll tackle advanced topics like CUDA streams and learn how to create GPU libraries that can be used in Python projects. Each step is reinforced with hands-on examples and real-world scenarios.
Key Features
- Accelerate C++ applications by applying parallel programming with CUDA
- Optimize GPU resource usage for efficient, high-performance execution
- Build and distribute reusable GPU libraries for Python integration
Target Audience
Ideal for C++ developers and programmers who want to boost application performance through GPU acceleration. If you have solid C++ experience and are eager to dive into high-performance computing, you'll find practical value here. A basic understanding of operating systems will help when working with device memory and advanced GPU features.
