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I encountered a weird problem while using python multiprocessing library.
My code is sketched below: I spawn a process for each "symbol, date" tuple. I combine the results afterwards.
I expect that when a process has done computing for a "symbol, date" tuple, it should release its memory? apparently that's not the case. I see dozens of processes (though I set the process pool to have size 7) that are suspended¹ in the machine. They consume no CPU, and they don't release the memory.
How do I let a process release its memory, after it has done its computation?
Thanks!
¹ by "suspended" I mean their status in ps command is shown as "S+"
def do_one_symbol( symbol, all_date_strings ): pool = Pool(processes=7) results = []; for date in all_date_strings: res = pool.apply_async(work, [symbol, date]) results.append(res); gg = mm = ss = 0; for res in results: g, m, s = res.get() gg += g; mm += m; ss += s; 
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Shared memory can be a very efficient way of handling data in a program that uses concurrency. Python's mmap uses shared memory to efficiently share large amounts of data between multiple Python processes, threads, and tasks that are happening concurrently.
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Python provides a mutual exclusion lock for use with processes via the multiprocessing. Lock class. An instance of the lock can be created and then acquired by processes before accessing a critical section, and released after the critical section. Only one process can have the lock at any time.
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Did you try to close pool by using pool.close and then wait for process to finish by pool.join, because if parent process keeps on running and does not wait for child processes they will become zombies
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