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Python Parallel Programming Cookbook

Python Parallel Programming Cookbook

By : Zaccone
4.1 (11)
Buy this Book
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Python Parallel Programming Cookbook

Python Parallel Programming Cookbook

4.1 (11)
By: Zaccone
Buy this Book

Overview of this book

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.
Table of Contents (8 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Sections
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Conventions
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Reader feedback
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Customer support
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1
1. Getting Started with Parallel Computing and Python
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1. Getting Started with Parallel Computing and Python
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Introduction
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The parallel computing memory architecture
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Memory organization
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Parallel programming models
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How to design a parallel program
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How to evaluate the performance of a parallel program
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Introducing Python
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Python in a parallel world
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Introducing processes and threads
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Start working with processes in Python
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Start working with threads in Python
2
2. Thread-based Parallelism
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2. Thread-based Parallelism
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Introduction
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Using the Python threading module
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How to define a thread
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How to determine the current thread
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How to use a thread in a subclass
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Thread synchronization with Lock and RLock
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Thread synchronization with RLock
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Thread synchronization with semaphores
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Thread synchronization with a condition
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Thread synchronization with an event
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Using the with statement
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Thread communication using a queue
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Evaluating the performance of multithread applications
3
3. Process-based Parallelism
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3. Process-based Parallelism
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Introduction
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How to spawn a process
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How to name a process
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How to run a process in the background
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How to kill a process
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How to use a process in a subclass
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How to exchange objects between processes
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How to synchronize processes
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How to manage a state between processes
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How to use a process pool
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Using the mpi4py Python module
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Point-to-point communication
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Avoiding deadlock problems
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Collective communication using broadcast
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Collective communication using scatter
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Collective communication using gather
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Collective communication using Alltoall
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The reduction operation
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How to optimize communication
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4. Asynchronous Programming
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4. Asynchronous Programming
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Introduction
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Using the concurrent.futures Python modules
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Event loop management with Asyncio
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Handling coroutines with Asyncio
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Task manipulation with Asyncio
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Dealing with Asyncio and Futures
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5. Distributed Python
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5. Distributed Python
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Introduction
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Using Celery to distribute tasks
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How to create a task with Celery
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Scientific computing with SCOOP
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Handling map functions with SCOOP
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Remote Method Invocation with Pyro4
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Chaining objects with Pyro4
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Developing a client-server application with Pyro4
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Communicating sequential processes with PyCSP
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Using MapReduce with Disco
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A remote procedure call with RPyC
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6. GPU Programming with Python
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6. GPU Programming with Python
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Introduction
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Using the PyCUDA module
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How to build a PyCUDA application
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Understanding the PyCUDA memory model with matrix manipulation
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Kernel invocations with GPUArray
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Evaluating element-wise expressions with PyCUDA
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The MapReduce operation with PyCUDA
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GPU programming with NumbaPro
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Using GPU-accelerated libraries with NumbaPro
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Using the PyOpenCL module
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How to build a PyOpenCL application
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Evaluating element-wise expressions with PyOpenCl
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Testing your GPU application with PyOpenCL
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Index
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Index

How to kill a process

It's possible to kill a process immediately using the terminate() method. Also, we use the is_alive() method to keep track of whether the process is alive or not.

How to do it...

In this example, a process is created with the target function foo(). After the start, we kill it with the terminate() function:

#kill a Process: Chapter 3: Process Based Parallelism
import multiprocessing
import time

def foo():
    print ('Starting function')
    time.sleep(0.1)
    print ('Finished function')

if __name__ == '__main__':
    p = multiprocessing.Process(target=foo)
    print ('Process before execution:', p, p.is_alive())
    p.start()
    print ('Process running:', p, p.is_alive())
    p.terminate()
    print ('Process terminated:', p, p.is_alive())
    p.join()
    print ('Process joined:', p, p.is_alive())
    print ('Process exit code:', p.exitcode)

The following is the output we get when...

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Python Parallel Programming Cookbook
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