On 12/04/14 01:07, Sturla Molden wrote:
>> ATM the only other way to work with
>> a data set that's larger than memory-divided-by-numcpus is to
>> explicitly set up shared memory, and this is *really* hard for
>> anything more complicated than a single flat array.
>
>
> Not difficult. You just go to my GitHub site and grab the code ;)
>
> (I have some problems running it on my MBP though, not sure why, but it
> used to work on Linux and Windows, and possibly still does.)
>
> https://github.com/sturlamolden/sharedmem-numpy
Hmm, today it works fine on my MBP too... Good. :)
import multiprocessing as mp
import numpy as np
import sharedmem as shm
def proc(qin, qout):
print("grabbing array from queue")
a = qin.get()
print(a)
print("putting array in queue")
b = shm.zeros(10)
print(b)
qout.put(b)
print("waiting for array to be updated by another process")
a = qin.get()
print(b)
if __name__ == "__main__":
qin = mp.Queue()
qout = mp.Queue()
p = mp.Process(target=proc, args=(qin,qout))
p.start()
a = shm.zeros(4)
qin.put(a)
b = qout.get()
b[:] = range(10)
qin.put(None)
p.join()
sturla$ python example.py
grabbing array from queue
[ 0. 0. 0. 0.]
putting array in queue
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
waiting for array to be updated by another process
[ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Sturla
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