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 problem with my code, or can it be the inherent limitation of Python?

Obviously, this is just a fragment of the code, but that's where Python spends two-thirds of the run-time. I hope if I wrote something really inefficient it might show up in this fragment:

UP = (0, -1)
RIGHT = (1, 0)
DOWN = (0, 1)
LEFT = (-1, 0)
all_directions = [UP, DOWN, RIGHT, LEFT]
# point is just a tuple (x, y)
def look_around(self):
    max_sugar_point = self.point
    max_sugar = self.world.sugar_map[self.point].level
    min_range = 0

    random.shuffle(self.all_directions)
    for r in range(1, self.vision+1):
        for d in self.all_directions:
            p = ((self.point[0] + r * d[0]) % self.world.surface.length,
                (self.point[1] + r * d[1]) % self.world.surface.height)
            if self.world.occupied(p): # checks if p is in a lookup table (dict)
                continue
            if self.world.sugar_map[p].level > max_sugar:
                max_sugar = self.world.sugar_map[p].level
                max_sugar_point = p
    if max_sugar_point is not self.point:
        self.move(max_sugar_point)

Roughly equivalent code in NetLogo (this fragment does a bit more than the Python function above):

; -- The SugarScape growth and motion procedures. --
to M    ; Motion rule (page 25)
    locals [ps p v d]
    set ps (patches at-points neighborhood) with [count turtles-here = 0]
    if (count ps > 0) [
        set v psugar-of max-one-of ps [psugar]              ; v is max sugar w/in vision
        set ps ps with [psugar = v]                         ; ps is legal sites w/ v sugar
        set d distance min-one-of ps [distance myself]      ; d is min dist from me to ps agents
        set p random-one-of ps with [distance myself = d]   ; p is one of the min dist patches
        if (psugar >= v and includeMyPatch?) [set p patch-here]
        setxy pxcor-of p pycor-of p                         ; jump to p
        set sugar sugar + psugar-of p                       ; consume its sugar
        ask p [setpsugar 0]                                 ; .. setting its sugar to 0
    ]
    set sugar sugar - metabolism    ; eat sugar (metabolism)
    set age age + 1
end

On my computer, the Python code takes 15.5 sec to run 1000 steps; on the same laptop, the NetLogo simulation running in Java inside the browser finishes 1000 steps in less than 6 sec.

EDIT: Just checked Repast, using Java implementation. And it's also about the same as NetLogo at 5.4 sec. Recent comparisons between Java and Python suggest no advantage to Java, so I guess it's just my code that's to blame?

EDIT: I understand MASON is supposed to be even faster than Repast, and yet it still runs Java in the end.

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.