Multiplexing Exercises

Monte Carlo π

A simple toy problem to get a handle on multiple engines is a Monte Carlo approximation of π.

Let’s say we have a dartboard with a round target inscribed on a square board. If you threw darts randomly, and they land evenly distributed on the square board, how many darts would you expect to hit the target?

from random import random
from math import pi

def mcpi(nsamples):
    s = 0
    for i in xrange(nsamples):
        x = random()
        y = random()
        if x*x + y*y <= 1:
            s+=1
    return 4.*s/nsamples

def multi_mcpi(dview, nsamples):
    raise NotImplementedError("you write this")

It takes a lot of samples to get a good approximation. Can you write a function that will use your engines to break up the work?

mcpi solution

Remote Iterators

Can you create an object that iterates through a remote iterable?

In [10]: dview['a'] = range(5)

In [11]: ra = remote_iterator(dview, 'a')

remote iter solution