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.

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Distributed Computing with Python
Apr, 2016 | 170 pages
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Table of Contents (15 chapters)
Distributed Computing with Python
Distributed Computing with Python
Credits
Credits
About the Author
About the Author
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
An Introduction to Parallel and Distributed Computing
An Introduction to Parallel and Distributed Computing
Parallel computing
Distributed computing
Shared memory versus distributed memory
Amdahl's law
The mixed paradigm
Summary
2
Asynchronous Programming
Asynchronous Programming
Coroutines
An asynchronous example
Summary
3
Parallelism in Python
Parallelism in Python
Multiple threads
Multiple processes
Multiprocess queues
Closing thoughts
Summary
4
Distributed Applications – with Celery
Distributed Applications – with Celery
Establishing a multimachine environment
Installing Celery
Testing the installation
A tour of Celery
More complex Celery applications
Celery in production
Celery alternatives – Python-RQ
Celery alternatives – Pyro
Summary
5
Python in the Cloud
Python in the Cloud
Cloud computing and AWS
Creating an AWS account
Creating an EC2 instance
Storing data in Amazon S3
Amazon elastic beanstalk
Creating a private cloud
Summary
6
Python on an HPC Cluster
Python on an HPC Cluster
Your typical HPC cluster
Job schedulers
Running a Python job using HTCondor
Running a Python job using PBS
Debugging
Summary
7
Testing and Debugging Distributed Applications
Testing and Debugging Distributed Applications
The big picture
Common problems – clocks and time
Common problems – software environments
Common problems – permissions and environments
Common problems – the availability of hardware resources
Challenges – the development environment
A useful strategy – logging everything
A useful strategy – simulating components
Summary
8
The Road Ahead
The Road Ahead
The first two chapters
The tools
The cloud and the HPC world
Debugging and monitoring
Where to go next
Index
Index

Creating an EC2 instance

After all this setup work, we are now ready to create our first virtual machine; start it in the geographical region of our choice (remember to create a key for every region you choose to use) and log in to the running instance. We will do all of this through the web console for now.

If you are not there already, go back to the AWS web console, log in as our user (remember that you can use https://<ACCOUNT NUMBER>.signin.aws.amazon.com/console/ URL), and click on the EC2 icon.

The page that opens up is the EC2 console, as shown in the following screenshot:

Click on the blue Launch Instance button toward the middle of the page, just under the Create Instance heading. Now, we will be guided through the process of creating and starting up a virtual machine. First, choose the Amazon Machine Image (AMI), that is to say, the base operating system and default set of software packages that come with your VM.

There are many possible configurations to choose from. For now...

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