Algorithms for processing graphical objects such as raster images and 3D point clouds can quickly become very compute-intensive. Many of these algorithms are excellent choices for parallel processing optimizations. Developing custom general-purpose GPU code in C++ is easier than you might think, and well-designed GPU algorithms can yield game-changing performance improvements. This is especially critical for real-time applications such as autonomous navigation, machine vision, and medical imaging. This talk will cover: • Using nVidia and Intel APIs with C++ to create high-performance native GPU kernels. • Techniques to maximize GPU throughput and avoid performance bottlenecks. • Combining GPU and CPU-based optimizations (SIMD, multithreading) for additional performance.