Hands On Gpu Programming With Python And Cuda

Download Hands On Gpu Programming With Python And Cuda full books in PDF, epub, and Kindle. Read online free Hands On Gpu Programming With Python And Cuda ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Hands-On GPU Programming with Python and CUDA

Hands-On GPU Programming with Python and CUDA
Author :
Publisher : Packt Publishing Ltd
Total Pages : 300
Release :
ISBN-10 : 9781788995221
ISBN-13 : 1788995228
Rating : 4/5 (228 Downloads)

Book Synopsis Hands-On GPU Programming with Python and CUDA by : Dr. Brian Tuomanen

Download or read book Hands-On GPU Programming with Python and CUDA written by Dr. Brian Tuomanen and published by Packt Publishing Ltd. This book was released on 2018-11-27 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 Description Hands-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 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.


Hands-On GPU Programming with Python and CUDA Related Books

Hands-On GPU Programming with Python and CUDA
Language: en
Pages: 300
Authors: Dr. Brian Tuomanen
Categories: Computers
Type: BOOK - Published: 2018-11-27 - Publisher: Packt Publishing Ltd

DOWNLOAD EBOOK

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
Hands-On GPU Computing with Python
Language: en
Pages: 441
Authors: Avimanyu Bandyopadhyay
Categories: Computers
Type: BOOK - Published: 2019-05-14 - Publisher: Packt Publishing Ltd

DOWNLOAD EBOOK

Explore GPU-enabled programmable environment for machine learning, scientific applications, and gaming using PuCUDA, PyOpenGL, and Anaconda Accelerate Key Featu
CUDA by Example
Language: en
Pages: 524
Authors: Jason Sanders
Categories: Computers
Type: BOOK - Published: 2010-07-19 - Publisher: Addison-Wesley Professional

DOWNLOAD EBOOK

CUDA is a computing architecture designed to facilitate the development of parallel programs. In conjunction with a comprehensive software platform, the CUDA Ar
Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA
Language: en
Pages: 373
Authors: Bhaumik Vaidya
Categories: Computers
Type: BOOK - Published: 2018-09-26 - Publisher: Packt Publishing Ltd

DOWNLOAD EBOOK

Discover how CUDA allows OpenCV to handle complex and rapidly growing image data processing in computer and machine vision by accessing the power of GPU Key Fea
CUDA Programming
Language: en
Pages: 592
Authors: Shane Cook
Categories: Computers
Type: BOOK - Published: 2012-11-13 - Publisher: Newnes

DOWNLOAD EBOOK

'CUDA Programming' offers a detailed guide to CUDA with a grounding in parallel fundamentals. It starts by introducing CUDA and bringing you up to speed on GPU