4. Motivation -Cont.
Solution:
Running more than one program at a
time.
Dividing the CPU attention among a
set of tasks.
Parallel Processing, Multiprocessing,
or Multitasking.
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5. Parallel Processing in
Python
Two main ways to run tasks:
Process forks
Spawned threads
Python built-in tools like: os.fork, threading,
queue, and multiprocessing.
Third Party domains offers more advanced
tools.
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6. Forking Processes
Traditional ways to structure parallel
tasks.
Straight forward way to start an
independent program.
What is forking?
Copying programs.
Python Module - os.fork
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11. Python Modules
Python Modules:
_thread module
threading modules
Both modules provide tools for
synchronizing access to shared objects
with locks.
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12. The _thread Module
Start new independent threads of
execution within a process.
Doesn't support OOP
Platform independent module.
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15. Synchronizing access to
shared objects and names
What is problem?
Objects and namespaces in a process
that span the life of threads are
shared by all spawned threads.
Solution:
Threads automatically come with a
cross-task communications
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17. Threading Module
Internally uses the _thread module to
implement objects that represent
threads and common synchronization
tools.
Manage threads with high-level class-
based objects.
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19. Interprocess
Communication - Overview
Other solutions don’t support cross-
program communication
Sockets, Pipes, and Signals
Enable performing Inter-Process
Communication (IPC)
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20. The multiprocessing
Module - Overview
Provide the best of processes and threads.
Platform independent.
Uses processes instead of threads.
Provide synchronizations tools.
Leverage the capacity of multiple
processors.
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