Setting up your Python environment for GPU programming can be a very delicate process. The Anaconda Python 2.7 distribution is suggested for both Windows and Linux users for the purposes of this text. First, we should ensure that we have the correct hardware for GPU programming; generally speaking, a 64-bit Windows or Linux PC with 4 gigabytes of RAM and any entry-level NVIDIA GPU from 2016 or later will be sufficient for our ends. Windows users should be careful in using a version of Visual Studio that works well with both the CUDA Toolkit and Anaconda (such as VS 2015), while Linux users should be particularly careful in the installation of their GPU drivers, and set up the appropriate environment variables in their .bashrc file. Furthermore, Windows users should create an appropriate launch script that will set up their environment for GPU programming and should use...
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Book Overview & Buying
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Table Of Contents
Hands-On GPU Programming with Python and CUDA
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Hands-On GPU Programming with Python and CUDA
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Overview of this book
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.
Table of Contents (15 chapters)
Preface
Free Chapter
Why GPU Programming?
Setting Up Your GPU Programming Environment
Getting Started with PyCUDA
Kernels, Threads, Blocks, and Grids
Streams, Events, Contexts, and Concurrency
Debugging and Profiling Your CUDA Code
Using the CUDA Libraries with Scikit-CUDA
The CUDA Device Function Libraries and Thrust
Implementation of a Deep Neural Network
Working with Compiled GPU Code
Performance Optimization in CUDA
Where to Go from Here
Assessment
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