This book is designed for complete beginners and people who have just started to learn parallel computing. It does not require any specific knowledge besides the basics of computer architecture, and experience with C/C++ programming is assumed. For deep learning enthusiasts, in Chapter 10, Deep Learning Acceleration with CUDA, Python-based sample code is also provided, hence some Python knowledge is expected for that chapter specifically. 

The code for this book is primarily developed and tested in a Linux environment. Hence, familiarity with the Linux environment is helpful. Any of the latest Linux flavors, such as CentOS or Ubuntu, are okay. The code can be compiled either using a makefile or the command line. The book primarily uses a free software stack, so there is no need to buy any software licenses.  Two key pieces of software that will be used throughout are the CUDA Toolkit and PGI Community Edition.

Since the book primarily covers the latest GPU features making use of CUDA 10.x, in order to fully exploit all the training material, the latest GPU architecture (Pascal onward) will be beneficial. While not all chapters require the latest GPU, having the latest GPU will help you to reproduce the results achieved in the book. Each chapter has a section on the preferred or must-have GPU architecture in the Technical requirements section. 

You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

1. Log in or register at www.packt.com.
2. Select the Support tab.
3. Click on Code Downloads.
4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

• WinRAR/7-Zip for Windows
• Zipeg/iZip/UnRarX for Mac
• 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Learn-CUDA-Programming. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://static.packt-cdn.com/downloads/9781788996242_ColorImages.pdf.

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Note that there is an asynchronous alternative to cudaMemcpy."

A block of code is set as follows:

#include<stdio.h>
#include<stdlib.h>

__global__ void print_from_gpu(void) {
    printf("Hello World! from thread [%d,%d] \
        From device\n", threadIdx.x,blockIdx.x);
}

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

int main(void) {
    printf("Hello World from host!\n");
    print_from_gpu<<<1,1>>>();
    cudaDeviceSynchronize();
    return 0;
}

Any command-line input or output is written as follows:

$ nvcc -o hello_world hello_world.cu

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "For Windows users, in the VS project properties dialog, you can specify your GPU's compute capability at CUDA C/C++ | Device | Code Generation."