Download Udemy - Learn Parallel Computing in Python torrent

Udemy - Learn Parallel Computing in Python

Torrent:	Udemy - Learn Parallel Computing in Python
Description:	Description 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 Who this course is for: 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. Requirements Some experience of programming in Python (enough if you know how to use functions, lists and dictionaries). Recent version of Python installed. Being able to do 50 pull ups while shouting a unique prime number on each rep (just kidding). Last Updated 2/2021
Downloads:	179
Category:	Other /Tutorials
Size:	1.9 GB
Show Files »	Learn Parallel Computing in Python [TutsNode.com] - Learn Parallel Computing in Python 9. Communication using Message passing 5. Multi Process implementation.mp4 (100.7 MB) 5. Multi Process implementation.srt (18.3 KB) 2. Examples of using Pipes and Queues.srt (13.3 KB) 6. Thread and Process Pools.srt (12.4 KB) 7. Process Pool Example Part 1.srt (11.3 KB) 3. Pipelining Example.srt (11.1 KB) 1. Communication with Pipes and Queues.srt (9.4 KB) 9. Shoelace algorithm explained (optional).html (0.3 KB) 4. Single Process implementation.srt (8.8 KB) 8. Process Pool Example Part 2.srt (7.4 KB) 1. Communication with Pipes and Queues.mp4 (59.2 MB) 4. Single Process implementation.mp4 (44.2 MB) 2. Examples of using Pipes and Queues.mp4 (42.7 MB) 7. Process Pool Example Part 1.mp4 (40.2 MB) 3. Pipelining Example.mp4 (37.2 MB) 8. Process Pool Example Part 2.mp4 (36.8 MB) 6. Thread and Process Pools.mp4 (35.8 MB) 4. Thread Synchronization with Mutexes 1. Why do we need Synchronization.srt (15.1 KB) 3. Adding Mutexes to Letter Count Implementation.srt (9.6 KB) 2. Adding Mutexes to Letter Count.srt (4.2 KB) 1. Why do we need Synchronization.mp4 (88.9 MB) 3. Adding Mutexes to Letter Count Implementation.mp4 (40.0 MB) 2. Adding Mutexes to Letter Count.mp4 (15.9 MB) 6. Synchronization using Condition Variables 1. Introduction to Condition Variables.srt (13.1 KB) 2. Condition Variables for Wait Groups.srt (11.4 KB) 3. Implementing Wait Groups with Condition Variables.srt (6.8 KB) 4. Using Wait Groups.srt (6.2 KB) 1. Introduction to Condition Variables.mp4 (80.4 MB) 2. Condition Variables for Wait Groups.mp4 (30.3 MB) 4. Using Wait Groups.mp4 (27.4 MB) 3. Implementing Wait Groups with Condition Variables.mp4 (17.6 MB) 1. Introduction 3. Links and Resources for this course.html (0.7 KB) 2. More on Parallel Computing.srt (9.3 KB) 1. Understanding Parallel Computing.srt (8.1 KB) 1. Understanding Parallel Computing.mp4 (77.9 MB) 2. More on Parallel Computing.mp4 (71.8 MB) 7. Barriers Explained 1. What’s a Barrier.srt (12.7 KB) 3. Matrix Multiplication Implementation.srt (10.9 KB) 5. Multi Threaded implementation with Barriers.srt (7.7 KB) 2. Example Barrier Application Matrix Multiplication.srt (7.0 KB) 4. Multi Threaded Matrix Multiplication with Barriers.srt (6.3 KB) 1. What’s a Barrier.mp4 (61.5 MB) 3. Matrix Multiplication Implementation.mp4 (41.2 MB) 5. Multi Threaded implementation with Barriers.mp4 (35.3 MB) 4. Multi Threaded Matrix Multiplication with Barriers.mp4 (30.3 MB) 2. Example Barrier Application Matrix Multiplication.mp4 (14.5 MB) 10. Avoiding Deadlocks 4. Train Deadlock Example Part 2.srt (12.7 KB) 1. Deadlocking Robots, Philosophers and Trains.srt (11.6 KB) 7. Solving Deadlocks using an Arbitrator.srt (11.6 KB) 6. Implementing Resource Hierarchy Solution.srt (9.7 KB) 5. Solving Deadlocks using Resource Hierarchy.srt (9.4 KB) 8. Implementing Arbitrator Solution.srt (8.2 KB) 2. Simple Deadlock Example.srt (6.9 KB) 3. Train Deadlock Example Part 1.srt (5.9 KB) 1. Deadlocking Robots, Philosophers and Trains.mp4 (85.8 MB) 7. Solving Deadlocks using an Arbitrator.mp4 (60.4 MB) 4. Train Deadlock Example Part 2.mp4 (49.6 MB) 5. Solving Deadlocks using Resource Hierarchy.mp4 (44.7 MB) 6. Implementing Resource Hierarchy Solution.mp4 (41.5 MB) 8. Implementing Arbitrator Solution.mp4 (40.5 MB) 2. Simple Deadlock Example.mp4 (21.8 MB) 3. Train Deadlock Example Part 1.mp4 (18.2 MB) 8. Memory Sharing between Processes 3. Process Memory Sharing in Practice.srt (12.6 KB) 1. How can Processes share Memory.srt (10.6 KB) 2. Using Process Memory Sharing.srt (6.7 KB) 3. Process Memory Sharing in Practice.mp4 (69.9 MB) 1. How can Processes share Memory.mp4 (56.2 MB) 2. Using Process Memory Sharing.mp4 (36.0 MB) 2. Creating Threads and Processes 2. Threads in Python and the GIL (Global Interpreter Lock).srt (11.9 KB) 1. Processes and Threads.srt (9.8 KB) 3. Creating our first Thread.srt (9.7 KB) 4. Processes in Python.srt (7.3 KB) 5. Creating our first Processes.srt (5.7 KB) 1. Processes and Threads.mp4 (60.9 MB) 2. Threads in Python and the GIL (Global Interpreter Lock).mp4 (31.2 MB) 3. Creating our first Thread.mp4 (26.5 MB) 5. Creating our first Processes.mp4 (17.4 MB) 4. Processes in Python.mp4 (16.3 MB) 3. Memory Sharing between Threads 2. Letter count Implementation.srt (11.6 KB) 3. Letter count using Memory Sharing.srt (6.9 KB) 1. Memory Sharing.srt (6.8 KB) 1. Memory Sharing.mp4 (47.8 MB) 2. Letter count Implementation.mp4 (37.7 MB) 3. Letter count using Memory Sharing.mp4 (26.1 MB) 5. Waiting for Completed tasks using Joins 1. Understanding Joins.srt (10.8 KB) 3. Concurrent File Search.srt (9.8 KB) 2. File Search Example.srt (8.9 KB) 1. Understanding Joins.mp4 (59.5 MB) 3. Concurrent File Search.mp4 (45.4 MB) 2. File Search Example.mp4 (29.6 MB) TutsNode.com.txt (0.1 KB) .pad 0 (0.1 KB) 1 (11.5 KB) 2 (252.8 KB) 3 (129.5 KB) 4 (141.4 KB) 5 (204.8 KB) 6 (128.9 KB) 7 (0.5 KB) 8 (96.9 KB) 9 (105.0 files
Added:	2021-02-21 11:07:59
Language:	English
Peers:	Seeders : 21 , Leechers : 15
Release name:	Udemy - Learn Parallel Computing in Python
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