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Distributed Computing with Python

Distributed Computing with Python

By : Pierfederici
4.3 (3)
Buy this Book
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Distributed Computing with Python

Distributed Computing with Python

4.3 (3)
By: Pierfederici
Buy this Book

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 (10 chapters)
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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. An Introduction to Parallel and Distributed Computing
1. An Introduction to Parallel and Distributed Computing
Parallel computing
Distributed computing
Shared memory versus distributed memory
Amdahl's law
The mixed paradigm
Summary
2
2. Asynchronous Programming
2. Asynchronous Programming
Coroutines
An asynchronous example
Summary
3
3. Parallelism in Python
chevron up
3. Parallelism in Python
Multiple threads
Multiple processes
Multiprocess queues
Closing thoughts
Summary
4
4. Distributed Applications – with Celery
4. 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
5. Python in the Cloud
5. 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
6. Python on an HPC Cluster
6. 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
7. Testing and Debugging Distributed Applications
7. 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
8. The Road Ahead
8. The Road Ahead
The first two chapters
The tools
The cloud and the HPC world
Debugging and monitoring
Where to go next
9
Index
Index

Chapter 3. Parallelism in Python

We mentioned threads, processes, and in general, parallel programming in the previous two chapters. We talked, at a very high level and very much in abstract terms, about how you can organize code so that some portions run in parallel, potentially on multiple CPUs or even multiple machines.

In this chapter, we will look at parallel programming in more detail and see which facilities Python offers us to make our code use more than one CPU or CPU core at the time (but always within the boundaries of a single machine). The main goal here will be speed for CPU-intensive problems, and responsiveness for I/O-intensive code.

The good news is that we can write parallel programs in Python using just modules in the standard library and nothing else. This is not to say that no external libraries and tools might be relevant—quite the opposite. It is just that the Standard Library is enough for what we will try and do in this chapter.

In this chapter, we...

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