Actually you can use multiprocessing.Queue objects in select.select. i.e. que = multiprocessing.Queue() (input,[],[]) = select.select([que._reader],[],[]) would select que only if it is ready to be read from. No documentation about it though. I was reading the source code of the multiprocessing.queue library (at linux it’s usually sth like /usr/lib/python2.6/multiprocessing/queue.py) to find it out. With Queue.Queue … Read more
Parallel.For doesn’t divide the input into n pieces (where n is the MaxDegreeOfParallelism); instead it creates many small batches and makes sure that at most n are being processed concurrently. (This is so that if one batch takes a very long time to process, Parallel.For can still be running work on other threads. See Parallelism … Read more
Expanding a bit on the good answer you already got, it helps if you understand what Linux-y systems do. They spawn new processes using fork(), which has two good consequences: All data structures existing in the main program are visible to the child processes. They actually work on copies of the data. The child processes … Read more
>90% of the time is spent on method ‘acquire’ of ‘thread.lock’ objects
There are a couple of syntax issues that I can see in your code: args in Process expects a tuple, you pass an integer, please change line 5 to: p = Process(target=self.RunProcess, args=(i,)) list.append is a method and arguments passed to it should be enclosed in (), not [], please change line 6 to: processes.append(p) … Read more
The reason is lack of fork() on Windows (which is not entirely true). Because of this, on Windows the fork is simulated by creating a new process in which code, which on Linux is being run in child process, is being run. As the code is to be run in technically unrelated process, it has … Read more
I’ve encounter the same problem myself, I’ve got the following solution: before calling p.start(), you may set p.daemon=True. Then as mentioned here python.org multiprocessing When a process exits, it attempts to terminate all of its daemonic child processes.
You’re creating new processes inside a loop, and then forgetting to close them once you’re done with them. As a result, there comes a point where you have too many open processes. This is a bad idea. You could fix this by using a context manager which automatically calls pool.terminate, or manually call pool.terminate yourself. … Read more
As @pvg said in a comment, a (bounded) queue is the natural way to mediate among a producer and consumers with different speeds, ensuring they all stay as busy as possible but without letting the producer get way ahead. Here’s a self-contained, executable example. The queue is restricted to a maximum size equal to the … Read more
The python documentation for multiprocessing blames the lack of os.fork() for the problems in Windows. It may be applicable here. See what happens when you import psyco. First, easy_install it: C:\Users\hughdbrown>\Python26\scripts\easy_install.exe psyco Searching for psyco Best match: psyco 1.6 Adding psyco 1.6 to easy-install.pth file Using c:\python26\lib\site-packages Processing dependencies for psyco Finished processing dependencies for … Read more