English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 2h 11m | 750 MB
Parallel programming unlocks a program’s ability to execute multiple instructions simultaneously, increases the overall processing throughput, and is key to writing faster and more efficient applications. Curious about how parallel programming works in the real world? In this course, join instructors Barron and Olivia Stone as they introduce the basics of parallel programming in Python, providing the foundational knowledge you need to write more efficient, performant code. Barron and Olivia explain concepts like threading and mutual exclusion in a fun and informative way, relating them to everyday activities you perform in the kitchen. To cement the ideas, they demo them in action using Python. Each lesson is short and practical, driving home the theory with hands-on techniques.
Topics include:
- Sequential vs. parallel computing
- Shared vs. distributed memory
- Using multiples threads and processes
- Basic stages of a thread lifecycle
- Preventing data races
- Enforcing mutual exclusion
- Locks
- Checking for liveness
Table of Contents
1 Learn parallel programming basics
2 What you should know
3 Exercise files
4 Sequential vs. parallel computing
5 Parallel computing architectures
6 Shared vs. distributed memory
7 Thread vs. process
8 Concurrent vs. parallel execution
9 Global interpreter lock Python demo
10 Multiple threads Python demo
11 Multiple processes Python demo
12 Execution scheduling
13 Execution scheduling Python demo
14 Thread lifecycle
15 Thread lifecycle Python demo
16 Daemon thread
17 Daemon thread Python demo
18 Data race
19 Data race Python demo
20 Mutual exclusion
21 Mutual exclusion Python demo
22 Reentrant lock
23 Rlock Python demo
24 Try lock
25 Non-blocking acquire Python demo
26 Read-write lock
27 Read-write lock Python demo
28 Deadlock
29 Deadlock Python demo
30 Abandoned lock
31 Abandoned lock Python demo
32 Starvation
33 Starvation Python demo
34 Livelock
35 Livelock Python demo
36 Next steps
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