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Home > Programming > Parallel Programming > Python Parallel Programming Cookbook
Python Parallel Programming Cookbook
Python Parallel Programming Cookbook

Python Parallel Programming Cookbook: Master efficient parallel programming to build powerful applications using Python

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Python Parallel Programming Cookbook

Chapter 2. Thread-based Parallelism

In this chapter, we will cover the following recipes:

  • How to use the Python threading module
  • How to define a thread
  • How to determine the current thread
  • How to use a thread in a subclass
  • Thread synchronization with Lock and RLock
  • Thread synchronization with semaphores
  • Thread synchronization with a condition
  • Thread synchronization with an event
  • How to use the with statement
  • Thread communication using a queue
  • Evaluating the performance of multithread applications
  • The criticality of multithreaded programming

Introduction

Currently, the most widely used programming paradigm for the management of concurrence in software applications is based on multithreading. Generally, an application is made by a single process that is divided into multiple independent threads, which represent activities of different types that run parallel and compete with each other.

Although such a style of programming can lead to disadvantages of use and problems that need to be solved, modern applications with the mechanism of multithreading are still used quite widely.

Practically, all the existing operating systems support multithreading, and in almost all programming languages, there are mechanisms that you can use to implement concurrent applications through the use of threads.

Therefore, multithreaded programming is definitely a good choice to achieve concurrent applications. However, it is not the only choice available—there are several other alternatives, some of which, inter alia, perform better on the definition...

Using the Python threading module

Python manages a thread via the threading package that is provided by the Python standard library. This module provides some very interesting features that make the threading-based approach a whole lot easier; in fact, the threading module provides several synchronization mechanisms that are very simple to implement.

The major components of the threading module are:

  • The thread object
  • The Lock object
  • The RLock object
  • The semaphore object
  • The condition object
  • The event object

In the following recipes, we examine the features offered by the threading library with different application examples. For the examples that follow, we will refer to the Python distribution 3.3 (even though Python 2.7 could be used).

How to define a thread

The simplest way to use a thread is to instantiate it with a target function and then call the start() method to let it begin its work. The Python module threading has the Thread() method that is used to run processes and functions in a different thread:

class threading.Thread(group=None,
                       target=None,
                       name=None,
                       args=(),
                       kwargs={})

In the preceding code:

  • group: This is the value of group that should be None; this is reserved for future implementations
  • target: This is the function that is to be executed when you start a thread activity
  • name: This is the name of the thread; by default, a unique name of the form Thread-N is assigned to it
  • args: This is the tuple of arguments that are to be passed to a target
  • kwargs: This is the dictionary of keyword arguments that are to be used for the target function

It is useful to spawn a thread and pass arguments to it that tell it what work to...

How to determine the current thread

Using arguments to identify or name the thread is cumbersome and unnecessary. Each Thread instance has a name with a default value that can be changed as the thread is created. Naming threads is useful in server processes with multiple service threads that handle different operations.

How to do it…

To determine which thread is running, we create three target functions and import the time module to introduce a suspend execution of two seconds:

import threading
import time

def first_function():
    print (threading.currentThread().getName()+\
           str(' is Starting \n'))
    time.sleep(2)
    print (threading.currentThread().getName()+\
           str( ' is Exiting \n'))
    return

def second_function():
    print (threading.currentThread().getName()+\
           str(' is Starting \n'))
    time.sleep(2)
    print (threading.currentThread().getName()+\
           str( ' is Exiting \n'))
    return

def...

How to use a thread in a subclass

To implement a new thread using the threading module, you have to do the following:

  • Define a new subclass of the Thread class
  • Override the _init__(self [,args]) method to add additional arguments
  • Then, you need to override the run(self [,args]) method to implement what the thread should do when it is started

Once you have created the new Thread subclass, you can create an instance of it and then start a new thread by invoking the start() method, which will, in turn, call the run() method.

How to do it…

To implement a thread in a subclass, we define the myThread class. It has two methods that must be overridden with the thread's arguments:

import threading
import time

exitFlag = 0

class myThread (threading.Thread):
    def __init__(self, threadID, name, counter):
        threading.Thread.__init__(self)
        self.threadID = threadID
        self.name = name
        self.counter = counter
    def run(self):
        print ("Starting " +...

Thread synchronization with Lock and RLock

When two or more operations belonging to concurrent threads try to access the shared memory and at least one of them has the power to change the status of the data without a proper synchronization mechanism a race condition can occur and it can produce invalid code execution and bugs and unexpected behavior. The easiest way to get around the race conditions is the use of a lock. The operation of a lock is simple; when a thread wants to access a portion of shared memory, it must necessarily acquire a lock on that portion prior to using it. In addition to this, after completing its operation, the thread must release the lock that was previously obtained so that a portion of the shared memory is available for any other threads that want to use it. In this way, it is evident that the impossibility of incurring races is critical as the need of the lock for the thread requires that at a given instant, only a given thread can use this part of the shared...

Introduction

Currently, the most widely used programming paradigm for the management of concurrence in software applications is based on multithreading. Generally, an application is made by a single process that is divided into multiple independent threads, which represent activities of different types that run parallel and compete with each other.

Although such a style of programming can lead to disadvantages of use and problems that need to be solved, modern applications with the mechanism of multithreading are still used quite widely.

Practically, all the existing operating systems support multithreading, and in almost all programming languages, there are mechanisms that you can use to implement concurrent applications through the use of threads.

Therefore, multithreaded programming is definitely a good choice to achieve concurrent applications. However, it is not the only choice available—there are several other alternatives, some of which, inter alia, perform better on the definition of...

Using the Python threading module

Python manages a thread via the threading package that is provided by the Python standard library. This module provides some very interesting features that make the threading-based approach a whole lot easier; in fact, the threading module provides several synchronization mechanisms that are very simple to implement.

The major components of the threading module are:

  • The thread object

  • The Lock object

  • The RLock object

  • The semaphore object

  • The condition object

  • The event object

In the following recipes, we examine the features offered by the threading library with different application examples. For the examples that follow, we will refer to the Python distribution 3.3 (even though Python 2.7 could be used).

How to define a thread

The simplest way to use a thread is to instantiate it with a target function and then call the start() method to let it begin its work. The Python module threading has the Thread() method that is used to run processes and functions in a different thread:

class threading.Thread(group=None,
                       target=None,
                       name=None,
                       args=(),
                       kwargs={})

In the preceding code:

  • group: This is the value of group that should be None; this is reserved for future implementations

  • target: This is the function that is to be executed when you start a thread activity

  • name: This is the name of the thread; by default, a unique name of the form Thread-N is assigned to it

  • args: This is the tuple of arguments that are to be passed to a target

  • kwargs: This is the dictionary of keyword arguments that are to be used for the target function

It is useful to spawn a thread and pass arguments to it that tell it what work...

How to determine the current thread

Using arguments to identify or name the thread is cumbersome and unnecessary. Each Thread instance has a name with a default value that can be changed as the thread is created. Naming threads is useful in server processes with multiple service threads that handle different operations.

How to do it…

To determine which thread is running, we create three target functions and import the time module to introduce a suspend execution of two seconds:

import threading
import time

def first_function():
    print (threading.currentThread().getName()+\
           str(' is Starting \n'))
    time.sleep(2)
    print (threading.currentThread().getName()+\
           str( ' is Exiting \n'))
    return

def second_function():
    print (threading.currentThread().getName()+\
           str(' is Starting \n'))
    time.sleep(2)
    print (threading.currentThread().getName()+\
           str( ' is Exiting \n'))
    return

def third_function():
    print (threading.currentThread...

How to use a thread in a subclass

To implement a new thread using the threading module, you have to do the following:

  • Define a new subclass of the Thread class

  • Override the _init__(self [,args]) method to add additional arguments

  • Then, you need to override the run(self [,args]) method to implement what the thread should do when it is started

Once you have created the new Thread subclass, you can create an instance of it and then start a new thread by invoking the start() method, which will, in turn, call the run() method.

How to do it…

To implement a thread in a subclass, we define the myThread class. It has two methods that must be overridden with the thread's arguments:

import threading
import time

exitFlag = 0

class myThread (threading.Thread):
    def __init__(self, threadID, name, counter):
        threading.Thread.__init__(self)
        self.threadID = threadID
        self.name = name
        self.counter = counter
    def run(self):
        print ("Starting " + self.name)
        print_time...

Thread synchronization with Lock and RLock

When two or more operations belonging to concurrent threads try to access the shared memory and at least one of them has the power to change the status of the data without a proper synchronization mechanism a race condition can occur and it can produce invalid code execution and bugs and unexpected behavior. The easiest way to get around the race conditions is the use of a lock. The operation of a lock is simple; when a thread wants to access a portion of shared memory, it must necessarily acquire a lock on that portion prior to using it. In addition to this, after completing its operation, the thread must release the lock that was previously obtained so that a portion of the shared memory is available for any other threads that want to use it. In this way, it is evident that the impossibility of incurring races is critical as the need of the lock for the thread requires that at a given instant, only a given thread can use this part of the shared...

Thread synchronization with RLock

If we want only the thread that acquires a lock to release it, we must use a RLock() object. Similar to the Lock() object, the RLock() object has two methods: acquire() and release(). RLock() is useful when you want to have a thread-safe access from outside the class and use the same methods from inside the class.

How to do it…

In the sample code, we introduced the Box class, which has the methods add() and remove(), respectively, that provide us access to the execute() method so that we can perform the action of adding or deleting an item, respectively. Access to the execute() method is regulated by RLock():

import threading
import time

class Box(object):
    lock = threading.RLock()
    def __init__(self):
        self.total_items = 0
    def execute(self,n):
        Box.lock.acquire()
        self.total_items += n
        Box.lock.release()
    def add(self):
        Box.lock.acquire()
        self.execute(1)
        Box.lock.release()
    def remove(self...
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Key benefits

  • 1. Design and implement efficient parallel software
  • 2. Master new programming techniques to address and solve complex programming problems
  • 3. Explore the world of parallel programming with this book, which is a go-to resource for different kinds of parallel computing tasks in Python, using examples and topics covered in great depth

Description

This book will teach you parallel programming techniques using examples in Python and will help you explore the many ways in which you can write code that allows more than one process to happen at once. Starting with introducing you to the world of parallel computing, it moves on to cover the fundamentals in Python. This is followed by exploring the thread-based parallelism model using the Python threading module by synchronizing threads and using locks, mutex, semaphores queues, GIL, and the thread pool. Next you will be taught about process-based parallelism where you will synchronize processes using message passing along with learning about the performance of MPI Python Modules. You will then go on to learn the asynchronous parallel programming model using the Python asyncio module along with handling exceptions. Moving on, you will discover distributed computing with Python, and learn how to install a broker, use Celery Python Module, and create a worker. You will understand anche Pycsp, the Scoop framework, and disk modules in Python. Further on, you will learnGPU programming withPython using the PyCUDA module along with evaluating performance limitations.

Who is this book for?

Python Parallel Programming Cookbook is intended for software developers who are well versed with Python and want to use parallel programming techniques to write powerful and efficient code. This book will help you master the basics and the advanced of parallel computing.

What you will learn

  • Synchronize multiple threads and processes to manage parallel tasks
  • Implement message passing communication between processes to build parallel applications
  • Program your own GPU cards to address complex problems
  • Manage computing entities to execute distributed computational tasks
  • Write efficient programs by adopting the event-driven programming model
  • Explore the cloud technology with DJango and Google App Engine
  • Apply parallel programming techniques that can lead to performance improvements
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Publication date : Aug 26, 2015
Length: 286 pages
Edition : 1st
Language : English
ISBN-13 : 9781785289583
Category :
Programming
Languages :
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Concepts :
Parallel Programming

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Publication date : Aug 26, 2015
Length: 286 pages
Edition : 1st
Language : English
ISBN-13 : 9781785289583
Category :
Programming
Languages :
Python
Concepts :
Parallel Programming

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Table of Contents

7 Chapters
1. Getting Started with Parallel Computing and Python Chevron down icon Chevron up icon
1. Getting Started with Parallel Computing and Python
Introduction
The parallel computing memory architecture
Memory organization
Parallel programming models
How to design a parallel program
How to evaluate the performance of a parallel program
Introducing Python
Python in a parallel world
Introducing processes and threads
Start working with processes in Python
Start working with threads in Python
2. Thread-based Parallelism Chevron down icon Chevron up icon
2. Thread-based Parallelism
Introduction
Using the Python threading module
How to define a thread
How to determine the current thread
How to use a thread in a subclass
Thread synchronization with Lock and RLock
Thread synchronization with RLock
Thread synchronization with semaphores
Thread synchronization with a condition
Thread synchronization with an event
Using the with statement
Thread communication using a queue
Evaluating the performance of multithread applications
3. Process-based Parallelism Chevron down icon Chevron up icon
3. Process-based Parallelism
Introduction
How to spawn a process
How to name a process
How to run a process in the background
How to kill a process
How to use a process in a subclass
How to exchange objects between processes
How to synchronize processes
How to manage a state between processes
How to use a process pool
Using the mpi4py Python module
Point-to-point communication
Avoiding deadlock problems
Collective communication using broadcast
Collective communication using scatter
Collective communication using gather
Collective communication using Alltoall
The reduction operation
How to optimize communication
4. Asynchronous Programming Chevron down icon Chevron up icon
4. Asynchronous Programming
Introduction
Using the concurrent.futures Python modules
Event loop management with Asyncio
Handling coroutines with Asyncio
Task manipulation with Asyncio
Dealing with Asyncio and Futures
5. Distributed Python Chevron down icon Chevron up icon
5. Distributed Python
Introduction
Using Celery to distribute tasks
How to create a task with Celery
Scientific computing with SCOOP
Handling map functions with SCOOP
Remote Method Invocation with Pyro4
Chaining objects with Pyro4
Developing a client-server application with Pyro4
Communicating sequential processes with PyCSP
Using MapReduce with Disco
A remote procedure call with RPyC
6. GPU Programming with Python Chevron down icon Chevron up icon
6. GPU Programming with Python
Introduction
Using the PyCUDA module
How to build a PyCUDA application
Understanding the PyCUDA memory model with matrix manipulation
Kernel invocations with GPUArray
Evaluating element-wise expressions with PyCUDA
The MapReduce operation with PyCUDA
GPU programming with NumbaPro
Using GPU-accelerated libraries with NumbaPro
Using the PyOpenCL module
How to build a PyOpenCL application
Evaluating element-wise expressions with PyOpenCl
Testing your GPU application with PyOpenCL
Index Chevron down icon Chevron up icon
Index

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Jeff Dec 09, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I'm an intermediate coder, and this book is helping me to the next level with multiprocessing, testing and general strategies and tactics for attacking a challenge. Not for beginners, but a great asset after the intro books!!!
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Delio Dec 04, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is a complete overview of parallel programming and distributed systems. It includes the right amount of well explained theory and enough practical exercises. Although the book focuses on Python and some types of programming might not be supported by other languages, this book is very useful to whoever wants to have a clear understanding of all available kinds of parallel and distributed computing.
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ruben Oct 13, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
For all the users I did not have enough experience with this language, I could experience that with this book it helpsme a lot to understand the procedures and everything about this.With the recipes I could develop the main idea of a system, I really recommend this book. for all the user or developers that like to develop withslanguage.You will understand anche Pycsp, the Scoop framework, and disk modules in Python. Further on, you will learnGPU programming withPython using the PyCUDA module along with evaluating performance limitations.
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Natester Oct 12, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
For readers already familiar with the Python cookbooks from Packt, you'll know the recipes in the cookbooks strive to share practical examples without getting into esoteric details. The "Python Parallel Programming Cookbook" is more than a cookbook: It includes introductions to core concepts of programming architectures and programming architectures. These introductions are necessary to take advantage of parallel processing (not just with Python).Cookbook examples are also supported with discussion of other parallel processing concepts when relevant. This is definitely helpful in understanding the "why" some recipes are efficient. (Disclaimer: I've not tried all of the recipes in the book, but the descriptions with the examples I tried were helpful.)Also an important note about this book: It does not start with a quick intro to the basics of the Python language or how to configure a Python environment. This book is definitely for developers familiar with Python that need to take Python to the next level of performance--a good one to have in the collection.
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Kent P Pflibsen Nov 08, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very workable examples. Easy to follow and practice the principles.
Amazon Verified review Amazon

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About the author

Profile icon Zaccone
Zaccone
Giancarlo Zaccone has over fifteen years' experience of managing research projects in the scientific and industrial domains. He is a software and systems engineer at the European Space Agency (ESTEC), where he mainly deals with the cybersecurity of satellite navigation systems. Giancarlo holds a master's degree in physics and an advanced master's degree in scientific computing. Giancarlo has already authored the following titles, available from Packt: Python Parallel Programming Cookbook (First Edition), Getting Started with TensorFlow, Deep Learning with TensorFlow (First Edition), and Deep Learning with TensorFlow (Second Edition).
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