max fraser - Page 28 - Developer IT

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

- by max

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.

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