Accelerating Applications with Parallel Algorithms - CUDA C++ Class Part 1

Learn modern CUDA C++ programming techniques to accelerate applications using parallel algorithms on NVIDIA GPUs in this comprehensive 2-hour video tutorial. Master the fundamentals of GPU programming through hands-on exercises covering execution spaces, iterators, memory management, and parallel algorithm implementation. Explore the Thrust library for high-performance parallel computing, understand the differences between serial and parallel processing approaches, and work with advanced concepts like stencil operations, MdSpan for multidimensional arrays, and transform iterators. Practice computing statistical operations such as median and variance calculations, implement segmented operations for data processing, and optimize memory usage across different memory spaces. Follow along with practical coding exercises and their solutions covering execution space configuration, median computation, variance calculation, multidimensional span operations, segmented sum and mean calculations, and efficient memory copying techniques. Access accompanying slides and Google Colab notebooks to run GPU exercises for free, making this tutorial ideal for C++ developers seeking to write clean, efficient, and idiomatic GPU code using NVIDIA's latest programming tools and best practices.