Summary

You have now been introduced to the concept of concurrent and parallel programming. It is about designing and structuring programming commands and instructions, so that different sections of the program can be executed in an efficient order, while sharing the same resources. Since time is saved when some commands and instructions are executed at the same time, concurrent programming provides significant improvements in program execution time, as compared to traditional sequential programming.

However, various factors need to be taken into consideration while designing a concurrent program. While there are specific tasks that can easily be broken down into independent sections that can be executed in parallel (embarrassingly parallel tasks), others require different forms of coordination between the program commands, so that shared resources are used correctly and efficiently. There are also inherently sequential tasks, in which no concurrency and parallelism can be applied to achieve program speedup. You should know the fundamental differences between these tasks, so that you can design your concurrent programs appropriately.

Recently, there was a paradigm shift that facilitated the implementation of concurrency into most aspects of the programming world. Now, concurrency can be found almost everywhere: desktop and mobile applications, video games, web and internet development, AI, and so on. Concurrency is still growing, and it is expected to keep growing in the future. It is therefore crucial for any experienced programmer to understand concurrency and its relevant concepts, and to know how to integrate those concepts into their applications.

Python, on the other hand, is one of the most (if not the most) popular programming languages. It provides powerful options in most sub-fields of programming. The combination of concurrency and Python is therefore one of the topics most worth learning and mastering in programming.

In the next chapter, on Amdahl's Law, we will discuss how significant the improvements in speedup that concurrency provides for our programs are. We will analyze the formula for Amdahl's Law, discussing its implications and considering Python examples.