Distributed Computing with Python > PACKT 원서리스트

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: Asynchronous Programming
Coroutines
An asynchronous example
Summary

3: Parallelism in Python
Multiple threads
Multiple processes
Multiprocess queues
Closing thoughts
Summary

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: 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
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
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 first two chapters
The tools
The cloud and the HPC world
Debugging and monitoring
Where to go next

Appendix A: Index