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.
Enroll now: Learn Parallel Computing in Python
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
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You can view and review the lecture materials indefinitely, like an on-demand channel.
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