Python Gensim how to make WMD similarity run faster with multiprocessing

I am trying to run gensim WMD similarity faster. Typically, this is what is in the docs: Example corpus:

    my_corpus = ["Human machine interface for lab abc computer applications",
>>>              "A survey of user opinion of computer system response time",
>>>              "The EPS user interface management system",
>>>              "System and human system engineering testing of EPS",
>>>              "Relation of user perceived response time to error measurement",
>>>              "The generation of random binary unordered trees",
>>>              "The intersection graph of paths in trees",
>>>              "Graph minors IV Widths of trees and well quasi ordering",
>>>              "Graph minors A survey"]

my_query = 'Human and artificial intelligence software programs'
my_tokenized_query =['human','artificial','intelligence','software','programs']

model = a trained word2Vec model on about 100,000 documents similar to my_corpus.
model = Word2Vec.load(word2vec_model)

from gensim import Word2Vec
from gensim.similarities import WmdSimilarity

def init_instance(my_corpus,model,num_best):
    instance = WmdSimilarity(my_corpus, model,num_best = 1)
    return instance
instance[my_tokenized_query]

the best matched document is "Human machine interface for lab abc computer applications" which is great.

However the function instance above takes an extremely long time. So I thought of breaking up the corpus into N parts and then doing WMD on each with num_best = 1, then at the end of it, the part with the max score will be the most similar.

    from multiprocessing import Process, Queue ,Manager

    def main( my_query,global_jobs,process_tmp):
        process_query = gensim.utils.simple_preprocess(my_query)

        def worker(num,process_query,return_dict):  
            instance=init_instance\
(my_corpus[num*chunk+1:num*chunk+chunk], model,1)
            x = instance[process_query][0][0]
            y = instance[process_query][0][1]
            return_dict[x] = y
        manager = Manager()
        return_dict = manager.dict()

        for num in range(num_workers):
            process_tmp = Process(target=worker, args=(num,process_query,return_dict))
            global_jobs.append(process_tmp)
            process_tmp.start()
        for proc in global_jobs:
            proc.join()

        return_dict = dict(return_dict)
        ind = max(return_dict.iteritems(), key=operator.itemgetter(1))[0]
        print corpus[ind]
        >>> "Graph minors A survey"

The problem I have with this is that, even though it outputs something, it doesn't give me a good similar query from my corpus even though it gets the max similarity of all the parts.

Am I doing something wrong?