My Library

University LibraryCatalogue

     
Limit search to items available for borrowing or consultation
Result Page: Previous Next
Can't find that book? Try BONUS+
 
Look for full text

Search Discovery

Search CARM Centre Catalogue

Search Trove

Add record to RefWorks

Cover Art
E-RESOURCE
Author Tuomanen, Brian, author.

Title Hands-On GPU Programming with Python and CUDA [electronic resource] / Tuomanen, Brian.

Published Packt Publishing, 2018.

Copies

Location Call No. Status
 UniM INTERNET resource    AVAILABLE
Edition 1st edition
Physical description 310 p.
Series Safari Books Online
Summary 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: 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 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. D...
Reproduction Electronic reproduction. Boston, MA : Safari, Available via World Wide Web. 2018.
System notes Mode of access: World Wide Web.
NOTES Copyright © 2018 Packt Publishing 2018
Issuing body notes Made available through: Safari, an O’Reilly Media Company.
Other author Safari, an O’Reilly Media Company.
Subject Electronic books.
ISBN 9781788993913
9781788995221
Standard Number 9781788993913