Amdahl's Law offers us a method to estimate the potential speedup in execution time of a task that we can expect from a system when its resources are improved. It illustrates that, as the resources of the system are improved, so is the execution time. However, the differential speedup when incrementing the resources strictly decreases, and the throughput speedup is limited by the sequential overhead of its program.

You also saw that in specific situations (namely, when only the number of processors increases), Amdahl's Law resembles the law of diminishing returns. Specifically, as the number of processors increases, the efficiency gained through the improvement decreases, and the speedup curve flattens out.

Lastly, this chapter showed that improvement through concurrency and parallelism is not always desirable, and detailed specifications are needed for an effective and efficient concurrent program.

With more knowledge of the extent to which concurrency can help to speed up our programs, we will now start to discuss the specific tools that Python provides to implement concurrency. Specifically, we will consider one of the main players in concurrent programming, threads, in the next chapter, including their application in Python programming.