Leveraging “Copy-on-Write” to Copy Data to Multipr

Question

I have a bit of multiprocessing Python code that looks a bit like this:

import time
from multiprocessing import Pool
import numpy as np

class MyClass(object):
    def __init__(self):
        self.myAttribute = np.zeros(100000000) # basically a big memory struct

    def my_multithreaded_analysis(self):
        arg_lists = [(self, i) for i in range(10)]
        pool = Pool(processes=10)
        result = pool.map(call_method, arg_lists)
        print result

    def analyze(self, i):
        time.sleep(10)
        return i ** 2

def call_method(args):
    my_instance, i = args
    return my_instance.analyze(i)


if __name__ == '__main__':
    my_instance = MyClass()
    my_instance.my_multithreaded_analysis()

After reading answers about how memory works in other StackOverflow answers such as this one Python multiprocessing memory usage I was under the impression that this would not use memory in proportion to how many processes I used for multiprocessing, since it is copy-on-write and I have not modified any of the attributes of my_instance. However, I do see high memory for all processes when I run top it says most of my processes are using a lot of memory (this is top output from OSX, but I can replicate on Linux).

My question is basically, am I interpreting this correctly in that my instance of MyClass is actually duplicated across the pool? And if so, how can I prevent this; should I just not use a construction like this? My goal is to reduce memory usage for a computational analysis.

PID   COMMAND      %CPU  TIME     #TH    #WQ  #PORT MEM    PURG   CMPRS  PGRP PPID STATE
2494  Python       0.0   00:01.75 1      0    7     765M   0B     0B     2484 2484 sleeping
2493  Python       0.0   00:01.85 1      0    7     765M   0B     0B     2484 2484 sleeping
2492  Python       0.0   00:01.86 1      0    7     765M   0B     0B     2484 2484 sleeping
2491  Python       0.0   00:01.83 1      0    7     765M   0B     0B     2484 2484 sleeping
2490  Python       0.0   00:01.87 1      0    7     765M   0B     0B     2484 2484 sleeping
2489  Python       0.0   00:01.79 1      0    7     167M   0B     597M   2484 2484 sleeping
2488  Python       0.0   00:01.77 1      0    7     10M    0B     755M   2484 2484 sleeping
2487  Python       0.0   00:01.75 1      0    7     8724K  0B     756M   2484 2484 sleeping
2486  Python       0.0   00:01.78 1      0    7     9968K  0B     755M   2484 2484 sleeping
2485  Python       0.0   00:01.74 1      0    7     171M   0B     594M   2484 2484 sleeping
2484  Python       0.1   00:16.43 4      0    18    775M   0B     12K    2484 2235 sleeping

Answer 1

Alternatively, to take advantage of forking's copy-on-write benefits, while preserving some semblance of encapsulation, you could leverage class-attributes and @classmethods over pure globals.

import time
from multiprocessing import Pool
import numpy as np

class MyClass(object):

    myAttribute = np.zeros(100000000) # basically a big memory struct
    # myAttribute is a class-attribute

    @classmethod
    def my_multithreaded_analysis(cls):
        arg_list = [i for i in range(10)]
        pool = Pool(processes=10)
        result = pool.map(analyze, arg_list)
        print result

    @classmethod
    def analyze(cls, i):
        time.sleep(10)
        # If you wanted, you could access cls.myAttribute w/o worry here.
        return i ** 2

""" We don't need this proxy step !
    def call_method(args):
        my_instance, i = args
        return my_instance.analyze(i)
"""

if __name__ == '__main__':
    my_instance = MyClass()
    # Note that now you can instantiate MyClass anywhere in your app,
    # While still taking advantage of copy-on-write forking
    my_instance.my_multithreaded_analysis()

Note 1: Yes, I admit that class-attributes and class-methods are glorified globals. But it buys a bit of encapsulation...

Note 2: Rather than explicitly creating your arg_lists above, you can implicitly pass the instance (self) to each task created by Pool, by passing the bound-instance method analyze(self) to Pool.map(), and shoot yourself in the foot even easier!

Answer 2

Anything sent to pool.map (and related methods) isn't actually using shared copy-on-write resources. The values are "pickled" (Python's serialization mechanism), sent over pipes to the worker processes and unpickled there, which reconstructs the object in the child from scratch. Thus, each child in this case ends up with a copy-on-write version of the original data (which it never uses, because it was told to use the copy sent via IPC), and a personal recreation of the original data that was reconstructed in the child and is not shared.

If you want to take advantage of forking's copy-on-write benefits, you can't send data (or objects referencing the data) over the pipe. You have to store them in a location that can be found from the child by accessing their own globals. So for example:

import time
from multiprocessing import Pool
import numpy as np

class MyClass(object):
    def __init__(self):
        self.myAttribute = np.zeros(100000000) # basically a big memory struct

    def my_multithreaded_analysis(self):
        arg_lists = list(range(10))  # Don't pass self
        pool = Pool(processes=10)
        result = pool.map(call_method, arg_lists)
        print result

    def analyze(self, i):
        time.sleep(10)
        return i ** 2

def call_method(i):
    # Implicitly use global copy of my_instance, not one passed as an argument
    return my_instance.analyze(i)

# Constructed globally and unconditionally, so the instance exists
# prior to forking in commonly accessible location
my_instance = MyClass()


if __name__ == '__main__':
    my_instance.my_multithreaded_analysis()

By not passing self, you avoid making copies, and just use the single global object that was copy-on-write mapped into the child. If you needed more than one object, you might make a global list or dict mapping to instances of the object prior to creating the pool, then pass the index or key that can look up the object as part of the argument(s) to pool.map. The worker function then uses the index/key (which had to be pickled and sent to the child over IPC) to look up the value (copy-on-write mapped) in the global dict (also copy-on-write mapped), so you copy cheap information to lookup expensive data in the child without copying it.