This document provides an introduction to parallel programming using Python. It discusses the motivation for parallel programming being to utilize idle CPU capacity. The two main ways to run tasks in parallel in Python are using process forks and spawned threads. It then covers forking processes, threads, interprocess communication, and the multiprocessing module in Python for parallel programming.

1. Introduction to
Parallel Programming
using Python
Presented by: Samah Gad
July 11, 2013
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2. Road Map
Motivation
Forking Processes
Threads
Interprocess Communication - Overview
The multiprocessing Module - Overview
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3. Motivation
Problem:
Most computers spend a lot of time
doing nothing.
Majority of the modern CPU’s capacity
is often spent in an idle state.
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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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7. Example 1
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8. Example 2
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9. Threads
Another way to start activities
running at the same time.
Lightweight processes
Run within the same single process.
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10. Threads - Advantages:
Performance
Simplicity
Shared global memory
Portability
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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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13. Example 3
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14. Example 4
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15. Synchronizing access to
shared objects and names
What is the 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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16. Example 5
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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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18. 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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19. 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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20. Reference
Title: Programming Python
Author: Mark Lutz
Publisher:O'Reilly Media; Fourth Edition
edition (January 7, 2011)
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