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Python Parallel Programming Solutions [Video]

More Information
Learn
  • Synchronize multiple threads and processes to manage parallel tasks
  • Implement message passing communication between processes to build parallel applications
  • Program your own GPU cards to address complex problems
  • Manage computing entities to execute distributed computational tasks
  • Write efficient programs by adopting the event-driven programming model
  • Explore the cloud technology with DJango and Google App Engine
  • Apply parallel programming techniques that can lead to performance improvements
About

This course will teach you parallel programming techniques using examples in Python and help you explore the many ways in which you can write code that allows more than one process to happen at once.

Starting with introducing you to the world of parallel computing, we move on to cover the fundamentals in Python. This is followed by exploring the thread-based parallelism model using the Python threading module by synchronizing threads and using locks, mutex, semaphores queues, GIL, and the thread pool. Next you will be taught about process-based parallelism, where you will synchronize processes using message passing and will learn about the performance of MPI Python Modules.

Moving on, you’ll get to grips with the asynchronous parallel programming model using the Python asyncio module, and will see how to handle exceptions. You will discover distributed computing with Python, and learn how to install a broker, use Celery Python Module, and create a worker. You will understand anche Pycsp, the Scoop framework, and disk modules in Python. Further on, you will get hands-on in GPU programming with Python using the PyCUDA module and will evaluate performance limitations.

Style and Approach

A step-by-step guide to parallel programming using Python, with videos that feature one or more programming examples. It is a practically-oriented course and has all the necessary underlying parallel computing concepts.

Features
  • Design and implement efficient parallel software using the examples and topics covered in great depth
  • Master new programming techniques to address and solve complex programming problems
  • Explore the world of parallel programming with this course, your go-to resource for different kinds of parallel computing tasks in Python
Course Length 3 hours 59 minutes
ISBN 9781787280496
Date Of Publication 27 Dec 2016

Authors

Giancarlo Zaccone

Giancarlo Zaccone has over ten years of experience in managing research projects in scientific and industrial areas.

Giancarlo worked as a researcher at the CNR, the National Research Council of Italy. As part of his data science and software engineering projects, he gained experience in numerical computing, parallel computing, and scientific visualization. Currently, Giancarlo is a senior software and system engineer, based in the Netherlands. Here he tests and develops software systems for space and defense applications. Giancarlo holds a master's degree in Physics from the Federico II of Naples and a 2nd level postgraduate master course in Scienti fi c Computing from La Sapienza of Rome.

Giancarlo is the author of the following books: Python Parallel Programminng Cookbook, Getting Started with TensorFlow, Deep Learning with TensorFlow, all by Packt Publishing.