Book Image

Distributed Computing with Python

Book Image

Distributed Computing with Python

Overview of this book

CPU-intensive data processing tasks have become crucial considering the complexity of the various big data applications that are used today. Reducing the CPU utilization per process is very important to improve the overall speed of applications. This book will teach you how to perform parallel execution of computations by distributing them across multiple processors in a single machine, thus improving the overall performance of a big data processing task. We will cover synchronous and asynchronous models, shared memory and file systems, communication between various processes, synchronization, and more.
Table of Contents (15 chapters)
Distributed Computing with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

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!