Python中的Memoization,Classes和Multiprocessing

from multiprocessing import Pool
import sys
sys.setrecursionlimit(10000)
partitions = []
class Partitions:
    parts = {} #My goal is to use this dict to speed
               #up calculations in every process that
               #uses it,without having to build it up
               #from nothing each time
    def __init__(self):
        pass
    def p1(self,k,n):
        if (k,n) in Partitions.parts:
            return Partitions.parts[(k,n)]
        if k>n:
            return 0
        if k==n:
            return 1
        Partitions.parts[(k,n)] = self.p1(k+1,n) + self.p1(k,n-k)
        return Partitions.parts[(k,n)]

    def P(self,n):
        result = 0
        for k in xrange(1,n/2 + 1):
            result += self.p1(k,n-k)
        return 1 + result

p = Partitions()

def log(results):
    if results:
        partitions.extend(results)
    return None

def partWorker(start,stop):
    ps = []
    for n in xrange(start,stop):
        ps.append(((1,n),p.P(n)))
    return ps

def main():
    pool = Pool()
    step = 150
    for i in xrange(0,301,step):
        pool.apply_async(partWorker,(i,i+step),callback = log)

    pool.close()
    pool.join()

    return None

if __name__=="__main__":
    main()

我是新手,我基本上复制了此页面上主要代码的格式：
python prime crunching: processing pool is slower?
我是否可以在每个核心中运行进程,同时查看相同的字典以帮助他们
计算？它现在的行为方式,每个进程创建它自己的字典,它像疯了一样吃掉ram.