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Diffstat (limited to 'Doc/whatsnew')
| -rw-r--r-- | Doc/whatsnew/2.6.rst | 144 |
1 files changed, 134 insertions, 10 deletions
diff --git a/Doc/whatsnew/2.6.rst b/Doc/whatsnew/2.6.rst index c7c6ede..fe25e3b 100644 --- a/Doc/whatsnew/2.6.rst +++ b/Doc/whatsnew/2.6.rst @@ -526,28 +526,152 @@ environment variable. PEP 371: The ``multiprocessing`` Package ===================================================== -.. XXX I think this still needs help +The new :mod:`multiprocessing` package lets Python programs create new +processes that will perform a computation and return a result to the +parent. The parent and child processes can communicate using queues +and pipes, synchronize their operations using locks and semaphores, +and can share simple arrays of data. + +The :mod:`multiprocessing` module started out as an exact emulation of +the :mod:`threading` module using processes instead of threads. That +goal was discarded along the path to Python 2.6, but the general +approach of the module is still similar. The fundamental class +is the :class:`Process`, which is passed a callable object and +a collection of arguments. The :meth:`start` method +sets the callable running in a subprocess, after which you can call +the :meth:`is_alive` method to check whether the subprocess is still running +and the :meth:`join` method to wait for the process to exit. + +Here's a simple example where the subprocess will calculate a +factorial. The function doing the calculation is a bit strange; it's +written to take significantly longer when the input argument is a +multiple of 4. + +:: + + import time + from multiprocessing import Process, Queue + + + def factorial(queue, N): + "Compute a factorial." + # If N is a multiple of 4, this function will take much longer. + if (N % 4) == 0: + time.sleep(.05 * N/4) + + # Calculate the result + fact = 1L + for i in range(1, N+1): + fact = fact * i + + # Put the result on the queue + queue.put(fact) + + if __name__ == '__main__': + queue = Queue() + + N = 5 + + p = Process(target=factorial, args=(queue, N)) + p.start() + p.join() + + result = queue.get() + print 'Factorial', N, '=', result + +A :class:`Queue` object is created and stored as a global. The child +process will use the value of the variable when the child was created; +because it's a :class:`Queue`, parent and child can use the object to +communicate. (If the parent were to change the value of the global +variable, the child's value would be unaffected, and vice versa.) + +Two other classes, :class:`Pool` and :class:`Manager`, provide +higher-level interfaces. :class:`Pool` will create a fixed number of +worker processes, and requests can then be distributed to the workers +by calling :meth:`apply` or `apply_async`, adding a single request, +and :meth:`map` or :meth:`map_async` to distribute a number of +requests. The following code uses a :class:`Pool` to spread requests +across 5 worker processes, receiving a list of results back. + +:: + + from multiprocessing import Pool + + p = Pool(5) + result = p.map(factorial, range(1, 1000, 10)) + for v in result: + print v + +This produces the following output:: + + 1 + 39916800 + 51090942171709440000 + 8222838654177922817725562880000000 + 33452526613163807108170062053440751665152000000000 + ... + +The :class:`Manager` class creates a separate server process that can +hold master copies of Python data structures. Other processes can +then access and modify these data structures by using proxy objects. +The following example creates a shared dictionary by calling the +:meth:`dict` method; the worker processes then insert values into the +dictionary. (No locking is done automatically, which doesn't matter +in this example. :class:`Manager`'s methods also include +:meth:`Lock`, :meth:`RLock`, and :meth:`Semaphore` to create shared locks. + +:: + + import time + from multiprocessing import Pool, Manager + + def factorial(N, dictionary): + "Compute a factorial." + # Calculate the result + fact = 1L + for i in range(1, N+1): + fact = fact * i -:mod:`multiprocessing` makes it easy to distribute work over multiple processes. -Its API is similar to that of :mod:`threading`. For example:: + # Store result in dictionary + dictionary[N] = fact - from multiprocessing import Process + if __name__ == '__main__': + p = Pool(5) + mgr = Manager() + d = mgr.dict() # Create shared dictionary - def long_hard_task(n): - print n * 43 + # Run tasks using the pool + for N in range(1, 1000, 10): + p.apply_async(factorial, (N, d)) - for i in range(10): - Process(target=long_hard_task, args=(i)).start() + # Mark pool as closed -- no more tasks can be added. + p.close() -will multiply the numbers between 0 and 10 times 43 and print out the result -concurrently. + # Wait for tasks to exit + p.join() + + # Output results + for k, v in sorted(d.items()): + print k, v + +This will produce the output:: + + 1 1 + 11 39916800 + 21 51090942171709440000 + 31 8222838654177922817725562880000000 + 41 33452526613163807108170062053440751665152000000000 + 51 1551118753287382280224243016469303211063259720016986112000000000000 .. seealso:: + The documentation for the :mod:`multiprocessing` module. + :pep:`371` - Addition of the multiprocessing package PEP written by Jesse Noller and Richard Oudkerk; implemented by Richard Oudkerk and Jesse Noller. + .. ====================================================================== .. _pep-3101: |