In an attempt to run my code faster I thought pypy would be just the job. However, I am finding that it is actually slower for some of my code.
Can someone help me understand why this is the case?
This is the call (a weighted sum) that I have identified which is slower in pypy, which is the main method within a class, at line 663 of functions.py.
It is called 500000 times.
def __call__(self, verbose=False):
if len(self.links) != self.weights.size:
raise Exception(f'Number of links ...')
super().check_links(len(self.links))
inputs = np.array([link.get_value() for link in self.links])
self.value = np.dot(inputs, self.weights)
return super().__call__(verbose)
Here is the snakeviz view of pypy run with cProfile
And here is the snakeviz view run with python
EDIT: 20210612
@mattip I took your advice and tried a dot product in standard python (sdot). Here are timings with numpy dot (ndot) for python and pypy.
It is good that pypy sdot (0.299) is faster than python sdot (0.749) and faster than both ndots (1.075/4.165). However, what surprises me is that sdot (python lists) is faster than ndot (numpy arrays), with the python interpreter.
Why is that? I had thought that numpy was supposed to be an optimised, fast package for this sort of thing.
Here is the code:
numpy dot product
def runndot(runs):
weightslist = [0.5, 0.5, 0.5, 0.5, 0.5]
weights = np.array(weightslist)
inputslist = [0.1, 0.1, 0.1, 0.1, 0.1]
inputs = np.array(inputslist)
for _ in range(runs):
value = np.dot(inputs, weights)
return value
python lists dot product
def runsdot(runs):
weights = [0.5, 0.5, 0.5, 0.5, 0.5]
inputs = [0.1, 0.1, 0.1, 0.1, 0.1]
for _ in range(runs):
value = dot(inputs, weights)
return value
def dot(inputs, weights):
sum = 0
for i in range(len(inputs)):
sum += inputs[i]*weights[i]
return sum