To improve my code which has one heavy loop I need a speed up. How can I implement multiprocessing for a code like this? (a is typical of size 2 and l up to 10)
for x1 in range(a**l): for x2 in range(a**l): for x3 in range(a**l): output[x1,x2,x3] = HeavyComputationThatIsThreadSafe1(x1,x2,x3) If the HeavyComputationThatIsThreadSafe1 function only uses arrays and not python objects, I would using a concurrent futures (or the python2 backport) ThreadPoolExecutor along with Numba (or cython) with the GIL released. Otherwise use a ProcessPoolExecutor.
See:
http://numba.pydata.org/numba-doc/latest/user/examples.html#multi-threading
You'd want to parallelize the calculation at the level of the outermost loop and and then fill output from the chunks resulting from each thread/process. This assumes the cost of doing so is much cheaper than the computation, which should be the case.
HeavyComputationThatIsThreadSafe1over a billion times. How many seconds does a single call toHeavyComputationThatIsThreadSafe1take? Take that number, multiply it by 1073741824 and divide by the number of cores you have. That gives you the absolute best-case scenario runtime you could achieve with multiprocessing.HeavyComputationThatiIsThreadSafein the original question you linked to. Even with the data size you mention, it only takes ~8GB of memory and 45s to go over all three nested loops, if you take some reasonable optimization sets.