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Complete Guide to Parallel and Concurrent Programming in Python

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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. In this course, join instructors Barron and Olivia Chiu 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.


01 - Introduction
  • 01 - Learn parallel programming basics
  • 02 - What you should know
  • 03 - Exercise files

  • 02 - 1. Parallel Computing Hardware
  • 01 - Sequential vs. parallel computing
  • 02 - Parallel computing architectures
  • 03 - Shared vs. distributed memory

  • 03 - 2. Threads and Processes
  • 01 - Thread vs. process
  • 02 - Concurrent vs. parallel execution
  • 03 - Global interpreter lock Python demo
  • 04 - Multiple threads Python demo
  • 05 - Multiple processes Python demo
  • 06 - Execution scheduling
  • 07 - Execution scheduling Python demo
  • 08 - Thread lifecycle
  • 09 - Thread lifecycle Python demo
  • 10 - Daemon thread
  • 11 - Daemon thread Python demo

  • 04 - 3. Mutual Exclusion
  • 01 - Data race
  • 02 - Data race Python demo
  • 03 - Mutual exclusion
  • 04 - Mutual exclusion Python demo

  • 05 - 4. Locks
  • 01 - Reentrant lock
  • 02 - RLock Python demo
  • 03 - Try lock
  • 04 - Non-blocking acquire Python demo
  • 05 - Read-write lock
  • 06 - Read-write lock Python demo

  • 06 - 5. Liveness
  • 01 - Deadlock
  • 02 - Deadlock Python demo
  • 03 - Abandoned lock
  • 04 - Abandoned lock Python demo
  • 05 - Starvation
  • 06 - Starvation Python demo
  • 07 - Livelock
  • 08 - Livelock Python demo

  • 07 - 6. Synchronization
  • 01 - Condition variable
  • 02 - Condition variable Python demo
  • 03 - Producer-consumer
  • 04 - Producer-consumer threads Python demo
  • 05 - Producer-consumer processes Python demo
  • 06 - Semaphore
  • 07 - Semaphore Python demo

  • 08 - 7. Barriers
  • 01 - Race condition
  • 02 - Race condition Python demo
  • 03 - Barrier
  • 04 - Barrier Python demo

  • 09 - 8. Asynchronous Tasks
  • 01 - Computational graph
  • 02 - Thread pool
  • 03 - Thread pool Python demo
  • 04 - Process pool Python demo
  • 05 - Future
  • 06 - Future Python demo
  • 07 - Divide and conquer
  • 08 - Divide and conquer Python demo

  • 10 - 9. Evaluating Parallel Performance
  • 01 - Speedup, latency, and throughput
  • 02 - Amdahls law
  • 03 - Measure speedup
  • 04 - Measure speedup Python demo

  • 11 - 10. Designing Parallel Programs
  • 01 - Partitioning
  • 02 - Communication
  • 03 - Agglomeration
  • 04 - Mapping

  • 12 - 11. Challenge Problems
  • 01 - Welcome to the challenges
  • 02 - Challenge Matrix multiply in Python
  • 03 - Solution Matrix multiply in Python
  • 04 - Challenge Merge sort in Python
  • 05 - Solution Merge sort in Python
  • 06 - Challenge Download images in Python
  • 07 - Solution Download images in Python

  • 13 - Conclusion
  • 01 - Additional resources
  • 02 - Next steps
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    تاریخ انتشار: ۱۴ دی ۱۴۰۴
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