Hands-On GPU Programming with Python and CUDA

Build real-world applications with Python 2.7, CUDA 9, and CUDA 10. We suggest the use of Python 2.7 over Python 3.x, since Python 2.7 has stable support across all the libraries we use in this book. Key Features Expand your background in GPU programmingPyCUDA, scikit-cuda, and Nsight Effectively use CUDA libraries such as cuBLAS, cuFFT, and cuSolver Apply GPU programming to modern data science applications Book DescriptionHands-On GPU Programming with Python and CUDA hits the ground running: youll start by learning how to apply Amdahls Law, use a code profiler to identify bottlenecks in your Python code, and set up an appropriate GPU programming environment. Youll then see how to query the GPUs features and copy arrays of data to and from the GPUs own memory. As you make your way through the book, youll launch code directly onto the GPU and write full blown GPU kernels and device functions in CUDA C. Youll get to grips with profiling GPU code effectively and fully test and debug your code using Nsight IDE. Next, youll explore some of the more well-known NVIDIA libraries, such as cuFFT and cuBLAS. With a solid background in place, you will now apply your new-found knowledge to develop your very own GPU-based deep neural network from scratch. Youll then explore advanced topics, such as warp shuffling, dynamic parallelism, and PTX assembly. In the final chapter, youll see some topics and applications related to GPU programming that you may wish to pursue, including AI, graphics, and blockchain. By the end of this book, you will be able to apply GPU programming to problems related to data science and high-performance computing. What you will learn Launch GPU code directly from Python Write effective and efficient GPU kernels and device functions Use libraries such as cuFFT, cuBLAS, and cuSolver Debug and profile your code with Nsight and Visual Profiler Apply GPU programming to datascience problems Build a GPU-based deep neuralnetwork from scratch Explore advanced GPU hardware features, such as warp shuffling Who this book is forHands-On GPU Programming with Python and CUDA is for developers and data scientists who want to learn the basics of effective GPU programming to improve performance using Python code. You should have an understanding of first-year college or university-level engineering mathematics and physics, and have some experience with Python as well as in any C-based programming language such as C, C++, Go, or Java.