Looking at the documentation for Pool.map it seems you’re almost correct: the chunksize parameter will cause the iterable to be split into pieces of approximately that size, and each piece is submitted as a separate task. So in your example, yes, map will take the first 10 (approximately), submit it as a task for a … Read more
Though my understanding is limited about this subject, from what I did I can tell there is one main difference between multiprocessing.Queue() and multiprocessing.Manager().Queue(): multiprocessing.Queue() is an object whereas multiprocessing.Manager().Queue() is an address (proxy) pointing to shared queue managed by the multiprocessing.Manager() object. therefore you can’t pass normal multiprocessing.Queue() objects to Pool methods, because it … Read more
Let’s look at the end of the program first. The multiprocessing module uses atexit to call multiprocessing.util._exit_function when your program ends. If you remove g2.next(), your program ends quickly. The _exit_function eventually calls Pool._terminate_pool. The main thread changes the state of pool._task_handler._state from RUN to TERMINATE. Meanwhile the pool._task_handler thread is looping in Pool._handle_tasks and … Read more
I did a lot of research, and couldn’t find a solution to fix the problem per se. But there is a decent work around that prevents the memory blowout for a small cost, worth especially on server side long running code. The solution essentially was to restart individual worker processes after a fixed number of … Read more
You can use the shared memory stuff from multiprocessing together with Numpy fairly easily: import multiprocessing import ctypes import numpy as np shared_array_base = multiprocessing.Array(ctypes.c_double, 10*10) shared_array = np.ctypeslib.as_array(shared_array_base.get_obj()) shared_array = shared_array.reshape(10, 10) #– edited 2015-05-01: the assert check below checks the wrong thing # with recent versions of Numpy/multiprocessing. That no copy is made … Read more
Trying again as I just saw the bounty 😉 Basically I think the error message means what it said – multiprocessing shared memory Arrays can’t be passed as arguments (by pickling). It doesn’t make sense to serialise the data – the point is the data is shared memory. So you have to make the shared … Read more
After a lot research and testing, I found that “Manager” does this job at a non-complex object level. The code below shows that object inst is shared between processes, which means property var of inst is changed outside when child process changes it. from multiprocessing import Process, Manager from multiprocessing.managers import BaseManager class SimpleClass(object): def … Read more
It seems like what you want is simple: multiprocessing.current_process(). For example: import multiprocessing def f(x): print multiprocessing.current_process() return x * x p = multiprocessing.Pool() print p.map(f, range(6)) Output: $ python foo.py <Process(PoolWorker-1, started daemon)> <Process(PoolWorker-2, started daemon)> <Process(PoolWorker-3, started daemon)> <Process(PoolWorker-1, started daemon)> <Process(PoolWorker-2, started daemon)> <Process(PoolWorker-4, started daemon)> [0, 1, 4, 9, 16, 25] … Read more
The OS sets up a hardware timer (Programmable interval timer or PIT) that generates an interrupt every N milliseconds. That interrupt is delivered to the kernel and user-code is interrupted. It works like any other hardware interrupt. For example your disk will force a switch to the kernel when it has completed an IO.
What you’re doing seems perfectly OK, provided you’re using the POSIX “raw” IO syscalls such as read(), write(), lseek() and so forth. If you use C stdio (fread(), fwrite() and friends) or some other language runtime library which has its own userspace buffering, then the answer by “Tilo” is relevant, in that due to the … Read more