It might be most sensible to use multiprocessing.Pool which produces a pool of worker processes based on the max number of cores available on your system, and then basically feeds tasks in as the cores become available.
The example from the standard docs (http://docs.python.org/2/library/multiprocessing.html#using-a-pool-of-workers) shows that you can also manually set the number of cores:
from multiprocessing import Pool
def f(x):
return x*x
if __name__ == '__main__':
pool = Pool(processes=4) # start 4 worker processes
result = pool.apply_async(f, [10]) # evaluate "f(10)" asynchronously
print result.get(timeout=1) # prints "100" unless your computer is *very* slow
print pool.map(f, range(10)) # prints "[0, 1, 4,..., 81]"
And it's also handy to know that there is the multiprocessing.cpu_count() method to count the number of cores on a given system, if needed in your code.
Edit: Here's some draft code that seems to work for your specific case:
import multiprocessing
def f(name):
print 'hello', name
if __name__ == '__main__':
pool = multiprocessing.Pool() #use all available cores, otherwise specify the number you want as an argument
for i in xrange(0, 512):
pool.apply_async(f, args=(i,))
pool.close()
pool.join()