multiprocessing

问题 I have python2.5 and multiprocessoring (get from http://code.google.com/p/python-multiprocessing/) This simple code (get from docs), works very strange from time to time, sometimes it ok, but sometimes it throw timeout ex or hang my Windows (Vista), only reset helps :) Why this can happen? from multiprocessing import Pool def f(x): print "fc",x return x*x pool = Pool(processes=4) if __name__ == '__main__': result = pool.apply_async(f, (10,)) # evaluate "f(10)" asynchronously print result.get

问题 I'm using multiprocessing.Pool in Python on Ubuntu 12.04, and I'm running into a curious problem; When I call map_async on my Pool, I spawn 8 processes, but they all struggle for dominance over a single core of my 8-core machine. The exact same code uses up both of my cores in my Macbook Pro, and all four cores of my other Ubuntu 12.04 desktop (as measured with htop , in all cases). My code is too long to post all of, but the important part is: P = multiprocessing.Pool() results = P.map_async

问题 I'm using multiprocessing.Pool in Python on Ubuntu 12.04, and I'm running into a curious problem; When I call map_async on my Pool, I spawn 8 processes, but they all struggle for dominance over a single core of my 8-core machine. The exact same code uses up both of my cores in my Macbook Pro, and all four cores of my other Ubuntu 12.04 desktop (as measured with htop , in all cases). My code is too long to post all of, but the important part is: P = multiprocessing.Pool() results = P.map_async

问题 I'm using multiprocessing.Pool in Python on Ubuntu 12.04, and I'm running into a curious problem; When I call map_async on my Pool, I spawn 8 processes, but they all struggle for dominance over a single core of my 8-core machine. The exact same code uses up both of my cores in my Macbook Pro, and all four cores of my other Ubuntu 12.04 desktop (as measured with htop , in all cases). My code is too long to post all of, but the important part is: P = multiprocessing.Pool() results = P.map_async

问题 I'm writing a server which uses multiprocessing.Process for each client. socket.accept() is being called in a parent process and the connection object is given as an argument to the Process. The problem is that when calling socket.close() the socket does not seem to be closing. The client's recv() should return immediately after close() has been called on the server. This is the case when using threading.Thread or just handle the requests in the main thread, however when using multiprocessing

问题 Technically when we execute the following code(recursive process.nexttick), the CPU usage would get to 100% or near. The question is the imagining that I'm running on a machine with one CPU and there's another process of node HTTP server working, how does it affect it? Does the thread doing recursive process.nexttick let the HTTP server work at all? If we have two threads of recursive process.nexttick, do they both get 50% share? Since I don't know any machine with one core cannot try it. And

问题 Technically when we execute the following code(recursive process.nexttick), the CPU usage would get to 100% or near. The question is the imagining that I'm running on a machine with one CPU and there's another process of node HTTP server working, how does it affect it? Does the thread doing recursive process.nexttick let the HTTP server work at all? If we have two threads of recursive process.nexttick, do they both get 50% share? Since I don't know any machine with one core cannot try it. And

问题 See example and execution result below: #!/usr/bin/env python3.4 from multiprocessing import Pool import time import os def initializer(): print("In initializer pid is {} ppid is {}".format(os.getpid(),os.getppid())) def f(x): print("In f pid is {} ppid is {}".format(os.getpid(),os.getppid())) return x*x if __name__ == '__main__': print("In main pid is {} ppid is {}".format(os.getpid(), os.getppid())) with Pool(processes=4, initializer=initializer) as pool: # start 4 worker processes result =

问题 See example and execution result below: #!/usr/bin/env python3.4 from multiprocessing import Pool import time import os def initializer(): print("In initializer pid is {} ppid is {}".format(os.getpid(),os.getppid())) def f(x): print("In f pid is {} ppid is {}".format(os.getpid(),os.getppid())) return x*x if __name__ == '__main__': print("In main pid is {} ppid is {}".format(os.getpid(), os.getppid())) with Pool(processes=4, initializer=initializer) as pool: # start 4 worker processes result =

问题 I have defined this function def writeonfiles(a,seed): random.seed(seed) f = open(a, "w+") for i in range(0,10): j = random.randint(0,10) #print j f.write(j) f.close() Where a is a string containing the path of the file and seed is an integer seed. I want to parallelize a simple program in such a way that each core takes one of the available paths that I give in, seeds its random generator and write some random numbers on that files, so, for example, if I pass the vector vector = [Test/file1