Python并行编程参考手册
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作者(意)吉安卡洛·扎克尼
出版社东南大学出版社
ISBN9787564170738
出版时间2017-04
版次1
装帧平装
开本16开
纸张胶版纸
页数261页
字数99999千字
定价66元
上书时间2024-12-03
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基本信息
书名:Python并行编程参考手册
定价：66.00元
作者:(意)吉安卡洛·扎克尼
出版社：东南大学出版社
出版日期：2017-04-01
ISBN：9787564170738
字数：348000
页码：261
版次：1
装帧：平装
开本：16开
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内容提要
对于开发人员而言，如今要想充分利用所有可用的计算资源来构建出高效的软件系统，并行编程技术是必不可少的技能。从多核到GPU系统，再到分布式架构，计算量繁重的程序都离不开编程工具和软件库。 《Python并行编程参考手册（）》首先简要介绍了并行编程，然后讲述了Python的基础知识，接着探究了基于线程的并行模型、采用同步线程的Python线程模块以及锁、互斥量、信号量队列、GIL和线程池的用法。
目录
PrefaceChapter 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 PythonChapter 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 applicationsChapter 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 AIItoall The reduction operation How to optimize communicationChapter 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 FuturesChapter 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 RPyCChapter 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 PyOpenCI Testing your GPU application with PyOpenCLIndex
作者介绍
Giancarlo Zaccone has more than 10 years of experience in managing research projects,both in scientific and industrial domains. He worked as a researcher at the National Research Council (CNR), where he was involved in a few parallel numerical computing and scientific visualization projects. He currently works as a software engineer at a consulting company, developing and maintaining software systems for space and defense applications. Giancarlo holds a master's degree in physics from the University of Naples Federico Ⅱ and has completed a second-level postgraduate master's program in scientific computing from the Sapienza University of Rome.
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