Distributed Computing with Python

Summary

We covered a lot of ground in this first chapter. We looked at parallelism and distributed computing. We saw some conceptual examples of both architectures and their pros and cons. We touched on their implications for memory access and noted that reality is oftentimes somewhere in between these two extremes. We finished the chapter by looking at Amdahl's law and its implications on scalability and the economics of throwing hardware at the problem. In the next chapters, we will put these concepts in practice and write some Python code!