Learn Parallel Computing in Python

Learn Parallel Computing in Python, available at $69.99, has an average rating of 4.72, with 46 lectures, based on 630 reviews, and has 3963 subscribers.

You will learn about Discover how to create responsive and high performance software. See how to use multithreading and multiprocessing for modeling certain types of problems. Develop programs with Python that are highly Concurrent and Parallel. Understand the advantages, limits and properties of Parallel computing. Improve your programming skills in Python with more advanced, mulithreading and multiprocessing topics. Learn about threads, processes, mutexes, barriers, waitgroups, queues, pipes, condition variables, deadlocks and more. This course is ideal for individuals who are Developers who want to take their career to the next level by improving their skills and learning about concurrent and parallel programming. or College students currently learning about parallel computing who want to see how concepts learned in class relate to practice. or Experienced developers that have struggled with this topic and want to give it another try using a different approach. or Delivery managers called Brian Holmes. It is particularly useful for Developers who want to take their career to the next level by improving their skills and learning about concurrent and parallel programming. or College students currently learning about parallel computing who want to see how concepts learned in class relate to practice. or Experienced developers that have struggled with this topic and want to give it another try using a different approach. or Delivery managers called Brian Holmes.

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Summary

Title: Learn Parallel Computing in Python

Price: $69.99

Average Rating: 4.72

Number of Lectures: 46

Number of Published Lectures: 46

Number of Curriculum Items: 46

Number of Published Curriculum Objects: 46

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Discover how to create responsive and high performance software.

See how to use multithreading and multiprocessing for modeling certain types of problems.

Develop programs with Python that are highly Concurrent and Parallel.

Understand the advantages, limits and properties of Parallel computing.

Improve your programming skills in Python with more advanced, mulithreading and multiprocessing topics.

Learn about threads, processes, mutexes, barriers, waitgroups, queues, pipes, condition variables, deadlocks and more.

Who Should Attend

Developers who want to take their career to the next level by improving their skills and learning about concurrent and parallel programming.

College students currently learning about parallel computing who want to see how concepts learned in class relate to practice.

Experienced developers that have struggled with this topic and want to give it another try using a different approach.

Delivery managers called Brian Holmes.

Target Audiences

Developers who want to take their career to the next level by improving their skills and learning about concurrent and parallel programming.

College students currently learning about parallel computing who want to see how concepts learned in class relate to practice.

Experienced developers that have struggled with this topic and want to give it another try using a different approach.

Delivery managers called Brian Holmes.

The mood in the meeting on the 12th floor of an international investment bank was as bleak as it gets. The developers of the firm met to discuss the best way forward after a critical core application failed and caused a system wide outage.

“Guys, we have a serious issue here. I found out that the outage was caused by a race condition in our code, introduced a while ago and triggered last night.” says Mark Adams, senior developer.

The room goes silent. The cars outside the floor to ceiling windows slowly and silently creep along in the heavy city traffic. The senior developers immediately understand the severity of the situation, realizing that they will now be working around the clock to fix the issue and sort out the mess in the datastore. The less experienced developers understand that a race condition is serious but don’t know exactly what causes it and therefore keep their mouths shut.

Eventually Brian Holmes, delivery manager, breaks the silence with “The application has been running for months without any problems, we haven’t released any code recently, how is it possible that the software just broke down?!”

Everyone shakes their heads and goes back to their desk leaving Brian in the room alone, puzzled. He takes out his phone and googles “race condition”.

Sound familiar? How many times have you heard another developer talking about using threads and concurrent programming to solve a particular problem but out of fear you stayed out of the discussion?

Here’s the little secret that senior developers will never share Multithreading and Multiprocessing programming is not much harder than normal programming. Developers are scared of concurrent programming because they think it is an advanced topic that only highly experienced developers get to play with.

This is far from the truth. Our minds are very much used to dealing with concurrency. In fact we do this in our everyday life without any problem but somehow we struggle to translate this into our code. One of the reasons for this is that we’re not familiar with the concepts and tools available to us to manage this concurrency. This course is here to help you understand how to use multithreading and multiprocessing tools and concepts to manage your parallel programming. It is designed to be as practical as possible. We start with some theory around parallelism and then explain how the operating system handles multiple processes and threads. Later we move on to explain the multiple tools available by solving example problems using concurrent programming.

In this course we use the Python language, however the concepts learned here can be applied to most programming languages.

All code in this course can be found on github, username/project: cutajarj/multithreadinginpython

Course Curriculum

Chapter 1: Introduction

Lecture 1: Understanding Parallel Computing

Lecture 2: More on Parallel Computing

Lecture 3: Links and Resources for this course

Chapter 2: Creating Threads and Processes

Lecture 1: Processes and Threads

Lecture 2: Threads in Python and the GIL (Global Interpreter Lock)

Lecture 3: Creating our first Thread

Lecture 4: Processes in Python

Lecture 5: Creating our first Processes

Chapter 3: Memory Sharing between Threads

Lecture 1: Memory Sharing

Lecture 2: Letter count Implementation

Lecture 3: Letter count using Memory Sharing

Chapter 4: Thread Synchronization with Mutexes

Lecture 1: Why do we need Synchronization?

Lecture 2: Adding Mutexes to Letter Count

Lecture 3: Adding Mutexes to Letter Count Implementation

Chapter 5: Waiting for Completed tasks using Joins

Lecture 1: Understanding Joins

Lecture 2: File Search Example

Lecture 3: Concurrent File Search

Chapter 6: Synchronization using Condition Variables

Lecture 1: Introduction to Condition Variables

Lecture 2: Condition Variables for Wait Groups

Lecture 3: Implementing Wait Groups with Condition Variables

Lecture 4: Using Wait Groups

Chapter 7: Barriers Explained

Lecture 1: What’s a Barrier?

Lecture 2: Example Barrier Application: Matrix Multiplication

Lecture 3: Matrix Multiplication Implementation

Lecture 4: Multi Threaded Matrix Multiplication with Barriers

Lecture 5: Multi Threaded implementation with Barriers

Chapter 8: Memory Sharing between Processes

Lecture 1: How can Processes share Memory?

Lecture 2: Using Process Memory Sharing

Lecture 3: Process Memory Sharing in Practice

Chapter 9: Communication using Message passing

Lecture 1: Communication with Pipes and Queues

Lecture 2: Examples of using Pipes and Queues

Lecture 3: Pipelining Example

Lecture 4: Single Process implementation

Lecture 5: Multi Process implementation

Lecture 6: Thread and Process Pools

Lecture 7: Process Pool Example Part 1

Lecture 8: Process Pool Example Part 2

Lecture 9: Shoelace algorithm explained (optional)

Chapter 10: Avoiding Deadlocks

Lecture 1: Deadlocking Robots, Philosophers and Trains

Lecture 2: Simple Deadlock Example

Lecture 3: Train Deadlock Example Part 1

Lecture 4: Train Deadlock Example Part 2

Lecture 5: Solving Deadlocks using Resource Hierarchy

Lecture 6: Implementing Resource Hierarchy Solution

Lecture 7: Solving Deadlocks using an Arbitrator

Lecture 8: Implementing Arbitrator Solution

Instructors

James Cutajar
Software Developer, Author, Instructor

Rating Distribution

1 stars: 3 votes

2 stars: 5 votes

3 stars: 36 votes

4 stars: 184 votes

5 stars: 402 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!