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def __init__(self, work_queue, word, number): super()__init__() selfwork_queue = work_queue selfword = word selfnumber = number def run(self): while True: try: filename = selfwork_queueget() selfprocess(filename) finally: selfwork_queuetask_done()

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The __init__() method must call the base class __init__() The work queue is the same queueQueue shared by all the threads We have made the run() method an in nite loop This is common for daemon threads, and makes sense here because we don t know how many les the thread must process At each iteration we call queueQueueget() to get the next le to work on This call will block if the queue is empty, and does not have to be protected by a lock because queueQueue handles that automatically for us Once we have a le we process it, and afterward we must tell the queue that we have done that particular job calling queueQueuetask_done() is essential to the correct working of queueQueuejoin() We have not shown the process() function, because apart from the def line, the code is the same as the code used in grepword-p-childpy from the previous = "" line to the end (443 ) One nal point to note is that included with the book s examples is grepwordmpy, a program that is almost identical to the grepword-tpy program reviewed here, but which uses the multiprocessing module rather than the threading module The code has just three differences: rst, we import multiprocessing instead of queue and threading; second, the Worker class inherits multiprocessingProcess instead of threadingThread; and third, the work queue is a multiprocessingJoinableQueue instead of a queueQueue The multiprocessing module provides thread-like functionality using forking on systems that support it (Unix), and child processes on those that don t (Windows), so locking mechanisms are not always required, and the processes will run on whatever processor cores the operating system has available The package provides several ways of passing data between processes, including using a queue that can be used to provide work for processes just like queueQueue can be used to provide work for threads.

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The two main reasons for implementing a parallel program are to obtain better performance and to solve larger problems Performance can be both modeled and measured, so in this section we will take a another look at parallel computations by giving some simple analytical models that illustrate some of the factors that influence the performance of a parallel program Consider a computation consisting of three parts: a setup section, a computation section, and a finalization section The total running time of this program on one PE is then given as the sum of the times for the three parts Equation 21

FIGURE 912 Tables to hold information about colleges and their deans As you can see, there is a one-to-many relationship between these tables, but how do we enforce this relationship as one-to-one in SQL Server For starters, the fact that the dean_id column in tbl_college is required means that we are enforcing the rule that there be at least one dean Additionally, because the dean_id column exists in tbl_college, each college can have only one dean But what about the other half of the one-toone Remember that each person can be dean of only one college We can enforce this using a trigger such as the one shown next

The chief bene t of the multiprocessing version is that it can potentially run faster on multicore machines than the threaded version since it can run its processes on as many cores as are available Compare this with the standard Python interpreter (written in C, sometimes called CPython) which has a GIL (Global Interpreter Lock) that means that only one thread can execute Python code at any one time This restriction is an implementation detail and does not necessarily apply to other Python interpreters such as Jython

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