Hands-On GPU Programming with Python and CUDA

Performance Optimization in CUDA

In this penultimate chapter, we will cover some fairly advanced CUDA features that we can use for low-level performance optimizations. We will start by learning about dynamic parallelism, which allows kernels to launch and manage other kernels on the GPU, and see how we can use this to implement quicksort directly on the GPU. We will learn about vectorized memory access, which can be used to increase memory access speedups when reading from the GPU's global memory. We will then look at how we can use CUDA atomic operations, which are thread-safe functions that can operate on shared data without thread synchronization or mutex locks. We will learn about Warps, which are fundamental blocks of 32 or fewer threads, ...