Multiprocessing in python

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5. Multiprocessing in python
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6. Introduction
What is a process?
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A process is an instance of a computer program that is being
executed.
It contains the program code and its current activity
process
Depending on the operating system (OS), a process may be
made up of multiple threads of execution that execute
instructions concurrently
In fact only one process can be executing at any one time on a
single-core CPU (unless using multithreading or other similar
technology).

7. Multiprocessing
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Multiprocessing is the use of two or more central processing units(CPUs)
within a single computer system
The term also refers to the ability of a system to support more than one
processor and/or the ability to allocate tasks between them.

8. Python Multiprocessing:
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As CPU manufacturers start adding more and more cores to their
processors, creating parallel code is a great way to improve performance.
There are a lot of options out there for parallel development (such as
functional languages or developer toolkits).
Along with the release of Python 2.6 came a new module called
multiprocessing
The multiprocessing module lets you write parallelized code using
processes in relatively simple code

9. Processes, Queues and
Locks:
● The multiprocessing module comes with plenty of built-in options for building
a parallel application
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Three most basic (and safest) are the Process, Queue and Lock classes
Processes:
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The Process is an abstraction that sets up another (Python) process, provides it
code to run and a way for the parent application to control execution
from multiprocessing import Process#import the Process class
def say_hello(name='world'):#create a function the process will run
print "Hello, %s" % name
p = Process(target=say_hello)#instantiate a Process object with the
function it should run
p.start()#The process will run and return it's result
p.join()#process to complete via p.join()

10. ●
If you want to pass arguments to the function, simply provide a args keyword
argument like so:
from multiprocessing import Process
def say_hello(name='world'):
print "Hello, %s" % name
p = Process(target=say_hello, args=('Daniel',))
p.start()
p.join()

11. Queues
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Queue objects are exactly what they sound like: a thread/process
safe,FIFO data structure
Usage:
from multiprocessing import Queue
q = Queue()
q.put('Why hello there!')
q.put(['a', 1, {'b': 'c'}])
q.get() # Returns 'Why hello there!'
q.get() # Returns ['a', 1, {'b': 'c'}]
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Queues are especially useful when passed as a parameter to a Process' target
function to enable the Process to consume (or return) data.

12. Locks
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Like Queue objects, Locks are relatively straightforward.
They allow your code to claim the lock, blocking other processes from
executing similar code until the process has completed and release the lock
usage:
from multiprocessing import Lock
l = Lock()
l.acquire()
print 'Ha! Only I can write to stdout!'
l.release()

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