Foundations of GPU Acceleration with CUDA C/C++

In modern software development, CPU processing power alone is often not enough to handle computationally heavy tasks efficiently. By harnessing the massive parallel processing power of GPUs using CUDA, you can dramatically accelerate your C and C++ applications. This text-based course guides you through the fundamental principles of accelerated computing. You will transition from writing standard sequential CPU code to designing and executing parallel programs that run directly on high-performance graphics hardware. What you'll learn: 1. Understand the core differences between CPU and GPU architectures and how parallel execution works. 2. Write and launch CUDA kernels to perform parallel calculations in C/C++. 3. Manage memory allocation and data transfer between the host CPU and the GPU device. 4. Apply Unified Memory techniques to simplify memory management in accelerated applications. 5. Analyze GPU code execution to identify performance bottlenecks and optimize throughput. 6. Implement foundational parallel programming patterns for efficient data processing. The course begins with essential terminology, hardware concepts, and the fundamentals of parallel design before moving into practical code structures and optimization strategies. Through clear written explanations and step-by-step code walkthroughs, you will build a solid foundation in accelerated computing. This course is designed for software developers and students with basic C or C++ knowledge who want to learn parallel programming. No prior experience with GPU computing or CUDA is required. Start reading today to unlock the full performance potential of modern hardware.