How to utilize all cores with python multiprocessing

I have been fiddling with Python's multiprocessing functionality for upwards of an hour now, trying to parallelize a rather complex graph traversal function using multiprocessing.Process and multiprocessing.Manager:

import networkx as nx
import csv
import time 
from operator import itemgetter
import os
import multiprocessing as mp

cutoff = 1

exclusionlist = ["cpd:C00024"]

DG = nx.read_gml("KeggComplete.gml", relabel=True)

for exclusion in exclusionlist:
    DG.remove_node(exclusion)

# checks if 'memorizedPaths exists, and if not, creates it
fn = os.path.join(os.path.dirname(__file__),
                  'memorizedPaths' + str(cutoff+1))
if not os.path.exists(fn):
    os.makedirs(fn)

manager = mp.Manager()
memorizedPaths = manager.dict()
filepaths = manager.dict()
degreelist = sorted(DG.degree_iter(),
                    key=itemgetter(1),
                    reverse=True)

def _all_simple_paths_graph(item, DG, cutoff, memorizedPaths, filepaths):
    source = item[0]
    uniqueTreePaths = []

    if cutoff < 1:
        return

    visited = [source]
    stack = [iter(DG[source])]

    while stack:
        children = stack[-1]
        child = next(children, None)

        if child is None:
            stack.pop()
            visited.pop()
        elif child in memorizedPaths:
            for path in memorizedPaths[child]:
                newPath = (tuple(visited) + tuple(path))
                if (len(newPath) <= cutoff) and
                    (len(set(visited) & set(path)) == 0):
                    uniqueTreePaths.append(newPath)
            continue
        elif len(visited) < cutoff:
            if child not in visited:
                visited.append(child)
                stack.append(iter(DG[child]))

                if visited not in uniqueTreePaths:
                    uniqueTreePaths.append(tuple(visited))
        else: # len(visited) == cutoff:
            if (visited not in uniqueTreePaths) and
                (child not in visited):
                uniqueTreePaths.append(tuple(visited + [child]))
            stack.pop()
            visited.pop()
    # writes the absolute path of the node path file into the hash table
    filepaths[source] = str(fn) + "/" + str(source) + "path.txt"
    with open (filepaths[source], "wb") as csvfile2:
        writer = csv.writer(csvfile2, delimiter=" ", quotechar="|")
        for path in uniqueTreePaths:
            writer.writerow(path)

    memorizedPaths[source] = uniqueTreePaths

############################################################################

if __name__ == '__main__':
    start = time.clock()

    for item in degreelist:
        test = mp.Process(target=_all_simple_paths_graph,
                          args=(DG, cutoff, item, memorizedPaths, filepaths))
        test.start()
        test.join()

end = time.clock()
print (end-start)

Currently - though luck and magic - it works (sort of). My problem is I'm only using 12 of my 24 cores.

Can someone explain why this might be the case? Perhaps my code isn't the best multiprocessing solution, or is it a feature of my architecture Intel Xeon CPU E5-2640 @ 2.50GHz x18 running on Ubuntu 13.04 x64?

EDIT:

I managed to get:

p = mp.Pool()
for item in degreelist:
    p.apply_async(_all_simple_paths_graph,
                  args=(DG, cutoff, item, memorizedPaths, filepaths))
p.close()
p.join()

Working, however, it's VERY SLOW! So I assume I'm using the wrong function for the job. hopefully it helps clarify exactly what I'm trying to accomplish!

EDIT2: .map attempt:

partialfunc = partial(_all_simple_paths_graph,
                      DG=DG,
                      cutoff=cutoff,
                      memorizedPaths=memorizedPaths,
                      filepaths=filepaths)
p = mp.Pool()
for item in processList:
    processVar = p.map(partialfunc, xrange(len(processList)))   
p.close()
p.join()

Works, is slower than singlecore. Time to optimize!