CODEWITHSUNDEEP
python
computmaxer
computmaxer

Reputation: 1713

Python multiprocessing Pool.map not faster than calling the function once

I have a very large list of strings (originally from a text file) that I need to process using python. Eventually I am trying to go for a map-reduce style of parallel processing.

I have written a "mapper" function and fed it to multiprocessing.Pool.map(), but it takes the same amount of time as simply calling the mapper function with the full set of data. I must be doing something wrong.

I have tried multiple approaches, all with similar results.

def initial_map(lines):
    results = []
    for line in lines:
        processed = # process line (O^(1) operation)
        results.append(processed)
    return results

def chunks(l, n):
    for i in xrange(0, len(l), n):
        yield l[i:i+n]

if __name__ == "__main__":
    lines = list(open("../../log.txt", 'r'))
    pool = Pool(processes=8)
    partitions = chunks(lines, len(lines)/8)
    results = pool.map(initial_map, partitions, 1)

So the chunks function makes a list of sublists of the original set of lines to give to the pool.map(), then it should hand these 8 sublists to 8 different processes and run them through the mapper function. When I run this I can see all 8 of my cores peak at 100%. Yet it takes 22-24 seconds.

When I simple run this (single process/thread):

lines = list(open("../../log.txt", 'r'))
results = initial_map(results)

It takes about the same amount of time. ~24 seconds. I only see one process getting to 100% CPU.

I have also tried letting the pool split up the lines itself and have the mapper function only handle one line at a time, with similar results.

def initial_map(line):
    processed = # process line (O^(1) operation)
    return processed

if __name__ == "__main__":
    lines = list(open("../../log.txt", 'r'))
    pool = Pool(processes=8)
    pool.map(initial_map, lines)

~22 seconds.

Why is this happening? Parallelizing this should result in faster results, shouldn't it?

Upvotes: 4

Views: 3075

Answers (1)

Erik Kaplun
Erik Kaplun

Reputation: 38217

If the amount of work done in one iteration is very small, you're spending a big proportion of the time just communicating with your subprocesses, which is expensive. Instead, try to pass bigger slices of your data to the processing function. Something like the following:

slices = (data[i:i+100] for i in range(0, len(data), 100)

def process_slice(data):
    return [initial_data(x) for x in data]

pool.map(process_slice, slices)

# and then itertools.chain the output to flatten it

(don't have my comp. so can't give you a full working solution nor verify what I said)

Edit: or see the 3rd comment on your question by @ubomb.

Upvotes: 1

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