Running multiple instances of a python program efficiently & economically?

I wrote a program that calls a function with the following prototype:

def Process(n):

    # the function uses data that is stored as binary files on the hard drive and 
    # -- based on the value of 'n' -- scans it using functions from numpy & cython.    
    # the function creates new binary files and saves the results of the scan in them.
    #
    # I optimized the running time of the function as much as I could using numpy &  
    # cython, and at present it takes about 4hrs to complete one function run on 
    # a typical winXP desktop (three years old machine, 2GB memory etc).

My goal is to run this function exactly 10,000 times (for 10,000 different values of 'n') in the fastest & most economical way. following these runs, I will have 10,000 different binary files with the results of all the individual scans. note that every function 'run' is independent (meaning, there is no dependency whatsoever between the individual runs).

So the question is this. having only one PC at home, it is obvious that it will take me around 4.5 years (10,000 runs x 4hrs per run = 40,000 hrs ~= 4.5 years) to complete all runs at home. yet, I would like to have all the runs completed within a week or two.

I know the solution would involve accessing many computing resources at once. what is the best (fastest / most affordable, as my budget is limited) way to do so? must I buy a strong server (how much would it cost?) or can I have this run online? in such a case, is my propritary code gets exposed, by doing so?

in case it helps, every instance of 'Process()' only needs about 500MB of memory. thanks.