Hands-On GPU Programming with Python and CUDA

Dr. Brian Tuomanen

ISBN 10: 1788993918 / ISBN 13: 9781788993913

Editore: Packt Publishing Limited, GB, 2018

Riguardo questo articolo

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: 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.

Codice articolo LU-9781788993913

Dati bibliografici

• Titolo: Hands-On GPU Programming with Python and CUDA
• Editore: Packt Publishing Limited, GB
• Data di pubblicazione: 2018
• Lingua: Inglese
• ISBN 10: 1788993918
• ISBN 13: 9781788993913
• Rilegatura: Paperback
• Condizione: New

Descrizione articolo

Riassunto

Build GPU-accelerated high performing applications with Python 2.7, CUDA 9, and open source libraries such as PyCUDA and scikit-cuda. We recommend the use of Python 2.7 as this version has stable support across all libraries used in this book.

Key Features

• Get to grips with GPU programming tools such as PyCUDA, scikit-cuda, and Nsight
• Explore CUDA libraries such as cuBLAS, cuFFT, and cuSolver
• Apply GPU programming to modern data science applications

Book Description

GPU programming is the technique of offloading intensive tasks running on the CPU for faster computing. Hands-On GPU Programming with Python and CUDA will help you discover ways to develop high performing Python apps combining the power of Python and CUDA.

This book will help you hit 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 a GPU programming environment. You'll then see how to query a GPU's features and copy arrays of data to and from its memory. As you make your way through the book, you'll run your code directly on the GPU and write full blown GPU kernels and device functions in CUDA C. You'll even get to grips with profiling GPU code and fully test and debug your code using Nsight IDE. Furthermore, the book covers some well-known NVIDIA libraries such as cuFFT and cuBLAS.

With a solid background in place, you'll be able to develop your very own GPU-based deep neural network from scratch, and explore advanced topics such as warp shuffling, dynamic parallelism, and PTX assembly. Finally, you'll touch up on topics and applications like AI, graphics, and blockchain.

By the end of this book, you'll be confident in solving problems related to data science and high-performance computing with GPU programming.

What you will learn

• Write effective and efficient GPU kernels and device functions
• Work with libraries such as cuFFT, cuBLAS, and cuSolver
• Debug and profile your code with Nsight and Visual Profiler
• Apply GPU programming to data science problems
• Build a GPU-based deep neural network from scratch
• Explore advanced GPU hardware features such as warp shuffling

Who this book is for

This book is for developers and data scientists who want to learn the basics of effective GPU programming to improve performance using Python code. Familiarity with mathematics and physics concepts along with some experience with Python and any C-based programming language will be helpful.

Table of Contents

1. Why GPU Programming?
2. Setting Up Your GPU Programming Environment
3. Getting Started with PyCUDA
4. Kernels, Threads, Blocks, and Grids
5. Streams, Events, Contexts, and Concurrency
6. Debugging and Profiling Your CUDA Code
7. Using the CUDA Libraries with Scikit-CUDA Draft complete
8. The CUDA Device Function Libraries and Thrust
9. Implementing a Deep Neural Network
10. Working with Compiled GPU Code
11. Performance Optimization in CUDA
12. Where to Go from Here