Part 2: Asynchronous Mapping

Asynchronous functions allow you to give different tasks to different members of the multiprocessing.Pool. However, giving functions one by one is not very efficient. It would be good to be able to combine mapping with asynchronous functions, i.e. be able to give different mapping tasks simultanously to the pool of workers. Fortunately, Pool.map_async provides exactly that - an asynchronous parallel map.

Create a new python script called and copy into it

from multiprocessing import Pool, current_process
import contextlib
import time

def sum( (x, y) ):
    """Return the sum of the arguments"""
    print("Worker %s is processing sum(%d,%d)" \
             % (current_process().pid, x, y) )
    return x+y

def product( (x, y) ):
    """Return the product of the arguments"""
    print("Worker %s is processing product(%d,%d)" \
             % (current_process().pid, x, y) )
    return x*y

if __name__ == "__main__":

    a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
    b = [11, 12, 13, 14, 15, 16, 17, 18, 19, 20]

    work = zip(a,b)

    # Now create a Pool of workers
    with contextlib.closing( Pool() ) as pool:
        sum_future = pool.map_async( sum, work )
        product_future = pool.map_async( product, work )


    total_sum = reduce( lambda x,y: x+y, sum_future.get() )
    total_product = reduce( lambda x,y: x+y, product_future.get() )

    print("Sum of sums of 'a' and 'b' is %d" % total_sum)
    print("Sum of products of 'a' and 'b' is %d" % total_product)

Running this script, e.g. via python should result in something like

Worker 843 is processing sum(1,11)
Worker 844 is processing sum(2,12)
Worker 845 is processing sum(3,13)
Worker 846 is processing sum(4,14)
Worker 844 is processing sum(5,15)
Worker 846 is processing sum(6,16)
Worker 843 is processing sum(7,17)
Worker 845 is processing sum(8,18)
Worker 846 is processing sum(9,19)
Worker 843 is processing sum(10,20)
Worker 845 is processing product(1,11)
Worker 844 is processing product(2,12)
Worker 843 is processing product(3,13)
Worker 844 is processing product(4,14)
Worker 845 is processing product(5,15)
Worker 846 is processing product(6,16)
Worker 844 is processing product(7,17)
Worker 845 is processing product(8,18)
Worker 846 is processing product(9,19)
Worker 843 is processing product(10,20)
Sum of sums of 'a' and 'b' is 210
Sum of products of 'a' and 'b' is 935

This script provides two functions, sum and product, which are mapped asynchronously using the Pool.map_async function. This is identical to the function that you used before, except now the map is performed asynchronously. This means that the resulting list is returned in a future (in this case, the futures sum_future and product_future. The results are waited for using the .wait() functions, remembering to make sure that we don’t exit the with block until all results are available. Then, the results of mapping are retrieved using the .get() function of the futures.


By default, the function divides the work over the pool of workers by assiging pieces of work one by one. In the example above, the work to be performed was;


The work was assigned one by one to the four workers on my computer, i.e. the first worker process was given sum(1,11), the second sum(2,12), the third sum(3,13) the then the fourth sum(4,14). The first worker to finish was then given sum(5,15), then the next given sum(6,16) etc. etc.

Giving work one by one can be very inefficient for quick tasks, as the time needed by a worker process to stop and get new work can be longer than it takes to actually complete the task. To solve this problem, you can control how many work items are handed out to each worker process at a time. This is known as chunking, and the number of work items is known as the chunk of work to perform.

You can control the number of work items to perform per worker (the chunk size) by setting the chunksize argument, e.g.

future_sum = pool.map_async( sum, work, chunksize=5 )

would suggest to pool that each worker be given a chunk of five pieces of work. Note that this is just a suggestion, and pool may decide to use a slightly smaller or larger chunk size depending on the amount of work and the number of workers available.

Modify your script and set the chunksize to 5 for both of the asynchronous maps for sum and product. Re-run your script. You should see something like;

Worker 1045 is processing sum(1,11)
Worker 1046 is processing sum(6,16)
Worker 1047 is processing product(1,11)
Worker 1048 is processing product(6,16)
Worker 1045 is processing sum(2,12)
Worker 1046 is processing sum(7,17)
Worker 1047 is processing product(2,12)
Worker 1048 is processing product(7,17)
Worker 1045 is processing sum(3,13)
Worker 1048 is processing product(8,18)
Worker 1047 is processing product(3,13)
Worker 1046 is processing sum(8,18)
Worker 1045 is processing sum(4,14)
Worker 1047 is processing product(4,14)
Worker 1046 is processing sum(9,19)
Worker 1048 is processing product(9,19)
Worker 1047 is processing product(5,15)
Worker 1046 is processing sum(10,20)
Worker 1045 is processing sum(5,15)
Worker 1048 is processing product(10,20)
Sum of sums of 'a' and 'b' is 210
Sum of products of 'a' and 'b' is 935

My laptop has four workers. The first worker is assigned the first five items of work, i.e. sum(1,11) to sum(5,15), and it starts by running sum(1,11), hence why sum(1,11) is printed first.

The next worker is given the next five items of work, i.e. sum(6,16) to sum(10,20), and starts by running sum(6,16), hence why sum(6,16) is printed second.

The next worker is given the next five items of work, i.e. product(1,11) to product(5,15), and it starts by running product(1,11), hence why this is printed third.

The last worker is given the next five items of work, i.e. product(6,16) to product(10,20), and it starts by running product(6,16), hence why this is printed fourth.

Once each worker has finished its first item of work, it moves onto its second. This is why sum(2,12), sum(7,17), product(2,12) and product(7,17) are printed next. Then, each worker moves onto its third piece of work etc. etc.

If you don’t specify the chunksize then it is equal to 1. When writing a new script you should experiment with different values of chunksize to find the value that gives best performance.


Edit your script written in answer to exercise 2 of Parallel Map/Reduce, in which you count all of the words used in all Shakespeare plays (e.g. an example answer is here).

Edit the script so that you use an asynchronous map to distribute the work over the pool. This will free up the master process to give feedback to the user of the script, e.g. to print a progress or status message while the work is running to reassure the user that the script has not frozen. For example

while not future.ready():
    print("Work is in progress...")

Add a status message to your script to reassure the user that your script hasn’t frozen while it is processing.

(note that you can call your script using python -u shakespeare/* to use the -u argument to stop Python from buffering text written to standard output)

If you get stuck or want inspiration, a possible answer is given here.

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