agent-based simulation: performance issue: Python vs NetLogo & Repast

Question

I\'m replicating a small piece of Sugarscape agent simulation model in Python 3. I found the performance of my code is ~3 times slower than that of NetLogo. Is it likely the pro

Answer 1

This probably won't give dramatic speedups, but you should be aware that local variables are quite a bit faster in Python compared to accessing globals or attributes. So you could try assigning some values that are used in the inner loop into locals, like this:

def look_around(self):
    max_sugar_point = self.point
    max_sugar = self.world.sugar_map[self.point].level
    min_range = 0

    selfx = self.point[0]
    selfy = self.point[1]
    wlength = self.world.surface.length
    wheight = self.world.surface.height
    occupied = self.world.occupied
    sugar_map = self.world.sugar_map
    all_directions = self.all_directions

    random.shuffle(all_directions)
    for r in range(1, self.vision+1):
        for dx,dy in all_directions:
            p = ((selfx + r * dx) % wlength,
                (selfy + r * dy) % wheight)
            if occupied(p): # checks if p is in a lookup table (dict)
                continue
            if sugar_map[p].level > max_sugar:
                max_sugar = sugar_map[p].level
                max_sugar_point = p
    if max_sugar_point is not self.point:
        self.move(max_sugar_point)

Function calls in Python also have a relatively high overhead (compared to Java), so you can try to further optimize by replacing the occupied function with a direct dictionary lookup.

You should also take a look at psyco. It's a just-in-time compiler for Python that can give dramatic speed improvements in some cases. However, it doesn't support Python 3.x yet, so you would need to use an older version of Python.