parallel file parsing, multiple CPU cores

Question

I asked a related but very general question earlier (see especially this response).

This question is very specific. This is all the code I care about:

result = {}
for line in open('input.txt'):
  key, value = parse(line)
  result[key] = value

The function parse is completely self-contained (i.e., doesn't use any shared resources).

I have Intel i7-920 CPU (4 cores, 8 threads; I think the threads are more relevant, but I'm not sure).

What can I do to make my program use all the parallel capabilities of this CPU?

I assume I can open this file for reading in 8 different threads without much performance penalty since disk access time is small relative to the total time.

Answer 1

This can be done using Ray, which is a library for writing parallel and distributed Python.

To run the code below, first create input.txt as follows.

printf "1\n2\n3\n4\n5\n6\n" > input.txt

Then you can process the file in parallel by adding the @ray.remote decorator to the parse function and executing many copies in parallel as follows

import ray
import time

ray.init()

@ray.remote
def parse(line):
    time.sleep(1)
    return 'key' + str(line), 'value'

# Submit all of the "parse" tasks in parallel and wait for the results.
keys_and_values = ray.get([parse.remote(line) for line in open('input.txt')])
# Create a dictionary out of the results.
result = dict(keys_and_values)

Note that the optimal way to do this will depend on how long it takes to run the parse function. If it takes one second (as above), then parsing one line per Ray task makes sense. If it takes 1 millisecond, then it probably makes sense to parse a bunch of lines (e.g., 100) per Ray task.

Your script is simple enough that the multiprocessing module can also be used, however as soon as you want to do anything more complicated or want to leverage multiple machines instead of just one machine, then it will be much easier with Ray.

See the Ray documentation.

Answer 2

cPython does not provide the threading model you are looking for easily. You can get something similar using the multiprocessing module and a process pool

such a solution could look something like this:

def worker(lines):
    """Make a dict out of the parsed, supplied lines"""
    result = {}
    for line in lines.split('\n'):
        k, v = parse(line)
        result[k] = v
    return result

if __name__ == '__main__':
    # configurable options.  different values may work better.
    numthreads = 8
    numlines = 100

    lines = open('input.txt').readlines()

    # create the process pool
    pool = multiprocessing.Pool(processes=numthreads)

    # map the list of lines into a list of result dicts
    result_list = pool.map(worker, 
        (lines[line:line+numlines] for line in xrange(0,len(lines),numlines) ) )

    # reduce the result dicts into a single dict
    result = {}
    map(result.update, result_list)