Hands-On GPU Programming with Python and CUDA: Boost your application's performance and productivity with CUDA: Explore high-performance parallel computing with CUDA

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Hands-On GPU Programming with Python and CUDA: Boost your application's performance and productivity with CUDA: Explore high-performance parallel computing with CUDA

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Build real-world applications by writing effective GPU code, CUDA kernels, and device functions with the latest features of Python 3.7, CUDA 9 and CUDA 10Key FeaturesExpand your background in GPU programming PyCUDA, scikit-cuda, and NsightEffectively use CUDA libraries such as cuBLAS, cuFFT, and cuSolverApply GPU programming to modern data science applicationsBook DescriptionHands-On GPU Programming with Python and CUDA hits the ground running: you ll start by learning how to apply Amdahl s Law, use a code profiler to identify bottlenecks in your Python code, and set up an appropriate GPU programming environment. You ll then see how to query the GPU s features and copy arrays of data to and from the GPU s own memory.As you make your way through the book, you ll launch code directly onto the GPU and write full blown GPU kernels and device functions in CUDA C. You ll get to grips with profiling GPU code effectively and fully test and debug your code using Nsight IDE. Next, you ll 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. You ll then explore advanced topics, such as warp shuffling, dynamic parallelism, and PTX assembly. In the final chapter, you ll 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 learnLaunch GPU code directly from PythonWrite effective and efficient GPU kernels and device functionsUse libraries such as cuFFT, cuBLAS, and cuSolverDebug and profile your code with Nsight and Visual ProfilerApply GPU programming to datascience problemsBuild a GPU-based deep neuralnetwork from scratchExplore advanced GPU hardware features, such as warp shufflingWho 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.Table of ContentsWhy GPU Programming?Setting Up Your GPU Programming Environment Getting Started with PyCUDA Kernels, Threads, Blocks, and Grids Streams, Events, Contexts, and ConcurrencyDebugging and Profiling Your CUDA Code Using the CUDA Libraries with Scikit-CUDA Draft completeThe CUDA Device Function Libraries and ThrustImplementing a Deep Neural Network Working with Compiled GPU Code Performance Optimization in CUDA Where to Go from Here