Mastering GPU Programming with C++ and CUDA: Build High-Performance Parallel Applications, Optimize CUDA Workloads, and Create GPU Libraries for Python Integration - Softcover

Cole, Ethan

Softcover

ISBN 13: 9798276307541

Publisher: Independently published, 2025

Synopsis

Master the full power of GPU acceleration and take your C++ applications to performance levels that CPUs alone simply can’t reach.
Mastering GPU Programming with C++ and CUDA is a practical, modern, and results-driven guide for developers who want to build lightning-fast parallel applications, optimize CUDA workloads, and even deliver GPU-powered libraries that can be used directly from Python.
Whether you're accelerating simulations, processing massive datasets, building real-time applications, or exploring high-performance computing, this book shows you exactly how to harness NVIDIA GPUs with confidence—and produce software that truly scales.
Unlock the Real Performance Potential of GPUs
Starting from the core principles of parallelism and GPU architecture, you’ll learn how to think like a GPU programmer. Step-by-step, you’ll write your first kernels, optimize memory usage, scale across multiple GPUs, and leverage advanced features like CUDA streams and asynchronous execution.
Build Skills That Translate to Real-World Impact
Through clear explanations, hands-on examples, and professional optimization techniques, you will learn how to:

1. Develop high-performance parallel applications in C++ and CUDA
2. Optimize kernels, memory transfers, and GPU resource usage
3. Analyze and eliminate bottlenecks with profiling tools
4. Scale workloads using multi-GPU and distributed models
5. Package GPU-powered code into reusable C++ libraries
6. Expose CUDA libraries to Python using PyBind11 for broader adoption

Go Beyond Tutorials—Build Production-Ready Solutions
This isn’t just a collection of code snippets. You’ll explore real engineering practices:

1. Modern C++ patterns for safer GPU resource management
2. API design and modular GPU library architecture
3. Deployment strategies for research, enterprise, and cloud environments
4. Best practices for maintainability, testing, and cross-platform builds

Expand Your Reach with Python Integration
With Python now dominating AI, data science, and research, the book teaches you how to connect your GPU-accelerated C++ code to Python—turning your work into reusable, scalable, and widely adoptable tools.
Who This Book Is For

1. C++ developers breaking into GPU computing
2. Engineers and researchers building high-performance applications
3. Professionals working in AI, simulation, finance, analytics, robotics, gaming, and scientific computing
4. Anyone who wants to unlock more speed, scalability, and efficiency from their software

No prior CUDA experience is required just solid C++ fundamentals and the desire to push performance boundaries.
Why This Book Stands Out
Unlike generic CUDA resources, this book:

1. Covers fundamentals and advanced optimization
2. Bridges C++ and Python for maximum versatility
3. Focuses on reusable, production-grade techniques
4. Prepares you for the fast-evolving GPU landscape, cloud platforms, and next-gen hardware

Edition details

• Publisher: Independently published
• Publication date: 2025
• Language: English
• ISBN 13: 9798276307541
• Binding: Paperback
• Number of pages: 256