Book Image

Advanced Python Programming

By : Dr. Gabriele Lanaro, Quan Nguyen, Sakis Kasampalis
Book Image

Advanced Python Programming

By: Dr. Gabriele Lanaro, Quan Nguyen, Sakis Kasampalis

Overview of this book

This Learning Path shows you how to leverage the power of both native and third-party Python libraries for building robust and responsive applications. You will learn about profilers and reactive programming, concurrency and parallelism, as well as tools for making your apps quick and efficient. You will discover how to write code for parallel architectures using TensorFlow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. With the knowledge of how Python design patterns work, you will be able to clone objects, secure interfaces, dynamically choose algorithms, and accomplish much more in high performance computing. By the end of this Learning Path, you will have the skills and confidence to build engaging models that quickly offer efficient solutions to your problems. This Learning Path includes content from the following Packt products: • Python High Performance - Second Edition by Gabriele Lanaro • Mastering Concurrency in Python by Quan Nguyen • Mastering Python Design Patterns by Sakis Kasampalis
Table of Contents (41 chapters)
Title Page
About Packt

Chapter 21. Race Conditions

In this chapter, we will discuss the concept of race conditions and their potential causes in the context of concurrency. The definition of critical section, which is a concept highly relevant to race conditions and concurrent programming, will also be covered. We will use some example code in Python to simulate race conditions and the solutions commonly used to address them. Finally, real-life applications that commonly deal with race conditions will be discussed.

The following topics will be covered in this chapter:

  • The basic concept of a race condition, and how it occurs in concurrent applications, along with the definition of critical sections
  • A simulation of a race condition in Python and how to implement race condition solutions
  • The real-life computer science concepts that commonly interact and work with race conditions