Python multiprocessing with pathos

Question

I am trying to use Python\'s pathos to designate computations into separate processes in order to accelerate it with multicore processor. My code is organized like:

Answer 1

Here's how I go about this - I put the function to be run in parallel outside the class and pass the object as an arg while calling pool.map. Then, I return the object to be reassigned.

from pathos.multiprocessing import ProcessingPool


def boo(args):
    b, things = args
    for thing in things:
        b.sum += b.foo(thing)
    return [b, b.sum]

class Bar:
    def __init__(self):
       self.sum = 0
    def foo(self, name):
       return len(str(name))

pool = ProcessingPool(2)
b1 = Bar()
b2 = Bar()
print(b1, b2)

results = pool.map(boo, [[b1, [12,3,456]],[b2, ['a','b','cde']]])

b1, b1s = results[0]
b2, b2s = results[1]
print(b1,b1s,b1.sum)
print(b2, b2s, b2.sum)

Output:

(<__main__.Bar instance at 0x10b341518>, <__main__.Bar instance at 0x10b341560>)
(<__main__.Bar instance at 0x10b3504d0>, 6, 6)
(<__main__.Bar instance at 0x10b350560>, 5, 5)

Note that b1 and b2 are no longer the same as what they were before calling map because copies of them were made to be passed, as described by @Mike McKerns. However, the values of all their attributes are intact because they were passed, returned and reassigned.