CUDA Performance & Optimization For Beginners: A Practical Guide to Profiling, Memory Optimization, Kernel Performance, and GPU Acceleration* (CUDA Programming Series)

Writing a CUDA program that works is only the beginning. Getting that program to use the GPU efficiently requires a different set of skills: measuring performance, identifying bottlenecks, understanding memory behavior, analyzing kernel execution, and making optimization decisions based on evidence rather than assumptions.
CUDA Performance & Optimization For Beginners provides a practical introduction to the principles and techniques used to analyze and improve CUDA application performance.
Building on fundamental CUDA programming concepts, the book introduces a systematic approach to performance optimization. You will learn how to measure GPU workloads, interpret profiling information, identify common performance bottlenecks, understand memory behavior, improve kernel execution, and evaluate whether an optimization actually produces meaningful results.
You will explore how to:

• Understand the fundamentals of CUDA performance
• Measure and establish meaningful performance baselines
• Profile CUDA applications and investigate bottlenecks
• Analyze kernel execution and GPU utilization
• Understand memory-access behavior and its effect on performance
• Improve global, shared, and other forms of GPU memory usage
• Reduce unnecessary data movement between CPU and GPU
• Improve kernel organization and execution efficiency
• Understand occupancy and resource utilization
• Identify common causes of CUDA performance problems
• Benchmark and validate optimization changes
• Develop a repeatable approach to CUDA performance tuning

Rather than presenting optimization as a collection of isolated tricks, this book emphasizes a disciplined process: measure, analyze, optimize, and verify. This approach helps readers understand why a workload is slow and determine which changes are most likely to improve it.
Whether you are learning CUDA optimization for the first time, improving existing GPU applications, or developing a stronger understanding of GPU performance engineering, this book provides a practical foundation for making CUDA workloads more efficient.
Learn how to stop guessing about GPU performance—and start optimizing based on evidence.