Hands-On GPU Programming with Python and CUDA: Explore high-performance parallel

Description: Hands-On GPU Programming with Python and CUDA by Dr. Brian Tuomanen GPUs are designed for maximum throughput, but are subject to low-level subtleties. In contrast, Python is a high-level language that favours ease of use over speed. In this book, we will combine the power of both Python and CUDA to help you create high performing Python applications by using open-source libraries such as PyCUDA and SciKit-CUDA. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description 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 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: 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 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. Author Biography Dr. Brian Tuomanen has been working with CUDA and general-purpose GPU programming since 2014. He received his bachelor of science in electrical engineering from the University of Washington in Seattle, and briefly worked as a software engineer before switching to mathematics for graduate school. He completed his PhD in mathematics at the University of Missouri in Columbia, where he first encountered GPU programming as a means for studying scientific problems. Dr. Tuomanen has spoken at the US Army Research Lab about general-purpose GPU programming and has recently led GPU integration and development at a Maryland-based start-up company. He currently works as a machine learning specialist (Azure CSI) for Microsoft in the Seattle area. Table of Contents Table of ContentsWhy GPU Programming?Setting Up Your GPU Programming EnvironmentGetting Started with PyCUDAKernels, Threads, Blocks, and GridsStreams, Events, Contexts, and ConcurrencyDebugging and Profiling Your CUDA CodeUsing 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 Long Description 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 programming--PyCUDA, scikit-cuda, and Nsight Effectively use CUDA libraries such as cuBLAS, cuFFT, and cuSolver Apply GPU programming to modern data science applications Book Description Hands-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 for Hands-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. Details ISBN1788993918 Author Dr. Brian Tuomanen Pages 310 Publisher Packt Publishing Limited Year 2018 ISBN-10 1788993918 ISBN-13 9781788993913 Publication Date 2018-11-27 Short Title Hands-On GPU Programming with Python and CUDA Language English Format Paperback UK Release Date 2018-11-27 Imprint Packt Publishing Limited Place of Publication Birmingham Country of Publication United Kingdom AU Release Date 2018-11-27 NZ Release Date 2018-11-27 Subtitle Explore high-performance parallel computing with CUDA DEWEY 005.275 Audience General We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:130807178;

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