Parallelize Python's reduce command

Question

In Python I'm running a command of the form

reduce(func, bigArray[1:], bigArray[0])

and I'd like to add parallel processing to speed it up.

I am aware I can do this manually by splitting the array, running processes on the separate portions, and combining the result.

However, given the ubiquity of running reduce in parallel, I wanted to see if there's a native way, or a library, that will do this automatically.

I'm running a single machine with 6 cores.

Answer 1

For anyone stumbling across this, I ended up writing a helper to do it

def parallelReduce(l, numCPUs, connection=None):

    if numCPUs == 1 or len(l) <= 100:
            returnVal= reduce(reduceFunc, l[1:], l[0])
            if connection != None:
                    connection.send(returnVal)
            return returnVal

    parent1, child1 = multiprocessing.Pipe()
    parent2, child2 = multiprocessing.Pipe()
    p1 = multiprocessing.Process(target=parallelReduce, args=(l[:len(l) // 2], numCPUs // 2, child1, ) )
    p2 = multiprocessing.Process(target=parallelReduce, args=(l[len(l) // 2:], numCPUs // 2 + numCPUs%2, child2, ) )
    p1.start()
    p2.start()
    leftReturn, rightReturn = parent1.recv(), parent2.recv()
    p1.join()
    p2.join()
    returnVal = reduceFunc(leftReturn, rightReturn)
    if connection != None:
            connection.send(returnVal)
    return returnVal

Note that you can get the number of CPUs with multiprocessing.cpu_count()

Using this function showed substantial performance increase over the serial version.