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Hands-On GPU Programming with Python and CUDA

person icon Dr. Brian Tuomanen

Hands-On GPU Programming with Python and CUDA

Explore high-performance parallel computing with CUDA

person icon Dr. Brian Tuomanen

ebook icon Packt Publishing

language icon Language - English

updated on icon Updated on Oct, 2020

category icon Development ,Data Science,

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This eBook includes

Formats : PDF, EPUB, MOBI (Downlodable)

Pages : 310

ISBN : 9781788995221

Language : English

About the Book

Book 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: 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.

Hands-On GPU Programming with Python and CUDA

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Author Details

Packt Publishing

<a href="https://market.tutorialspoint.com/author/dr_brian_tuomanen">Dr. Brian Tuomanen</a>

Packt are an established, trusted, and innovative global technical learning publisher, founded in Birmingham, UK with over eighteen years experience delivering rich premium content from ground-breaking authors and lecturers on a wide range of emerging and established technologies for professional development.

Packt’s purpose is to help technology professionals advance their knowledge and support the growth of new technologies by publishing vital user focused knowledge-based content faster than any other tech publisher, with a growing library of over 9,000 titles, in book, e-book, audio and video learning formats, our multimedia content is valued as a vital learning tool and offers exceptional support for the development of technology knowledge.

We publish on topics that are at the very cutting edge of technology, helping IT professionals learn about the newest tools and frameworks in a way that suits them.

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