Preface

The study of computer science should cover not only the principles on which computational processing is based, but should also reflect the current state of knowledge of these fields. Today, the technology requires that professionals from all branches of computer science know both the software and hardware whose interaction at all levels is the key to understanding the basics of computational processing.

For this reason, in this book, a special focus is given on the relationship between hardware architectures and software.

Until recently, programmers could rely on the work of the hardware designers, compilers, and chip manufacturers to make their software programs faster or more efficient without the need for changes.

This era is over. So now, if a program is to run faster, it must become a parallel program.

Although the goal of many researchers is to ensure that programmers are not aware of the parallel nature of the hardware for which they write their programs, it will take many years before this actually becomes possible. Nowadays, most programmers need to thoroughly understand the link between hardware and software so that the programs can be run efficiently on modern computer architectures.

To introduce the concepts of parallel programming, the Python programming language has been adopted. Python is fun and easy to use, and its popularity has grown steadily in recent years. Python was developed more than 10 years ago by Guido van Rossum, who derived Python's syntax simplicity and ease of use largely from ABC, which is a teaching language that was developed in the 80s.

In addition to this specific context, Python was created to solve real-life problems, and it borrows a wide variety of typical characteristics of programming languages, such as C ++, Java, and Scheme. This is one of its most remarkable features, which has led to its broad appeal among professional software developers, the scientific research industry, and computer science educators. One of the reasons why Python is liked so much is because it provides the best balance between the practical and conceptual approaches. It is an interpreted language, so you can start doing things immediately without getting lost in the problems of compilation and linking. Python also provides an extensive software library that can be used in all sorts of tasks ranging from the Web, graphics, and of course, parallel computing. This practical aspect is a great way to engage readers and allow them to carry out projects that are important in this book.

This book contains a wide variety of examples that are inspired by many situations, and these offer you the opportunity to solve real-life problems. This book examines the principles of software design for parallel architectures, insisting on the importance of clarity of the programs and avoiding the use of complex terminology in favor of clear and direct examples. Each topic is presented as part of a complete, working Python program, which is followed by the output of the program in question.

The modular organization of the various chapters provides a proven path to move from the simplest arguments to the most advanced ones, but this is also suitable for those who only want to learn a few specific issues.

I hope that the settings and content of this book are able to provide you with a useful contribution for your better understanding and dissemination of parallel programming techniques.