CODEWITHSUNDEEP
pythonperformancenumpy
zell
zell

Reputation: 10204

Strange performance results with numpy

I am doing a performance test in python. By indexing large arrays in different ways, I got quite different performance. Here is something from my current project.

import numpy as np
import time

N=100000
i=0
rep=1000

x1=np.random.randn(N,4)
x2=np.random.randn(4,N)
G1=np.random.randn(N,4,3)
G2=np.random.randn(4,N,3)
B=np.random.randn(N,3)


starttime=time.time()
for k in range(rep):
    x1[:,i]=(G1[:,i,:] * B[:,:]).sum(axis=1)
elapsedtime1=time.time()-starttime


starttime=time.time()
for k in range (rep):
    x2[i,:]=(G2[i,:,:] * B[:,:]).sum(axis=1)
elapsedtime2=time.time()-starttime

print ("elaplsedtime1= "+str(elapsedtime1))
print ("elaplsedtime2= "+str(elapsedtime2))
diff=(elapsedtime2-elapsedtime1)/elapsedtime2
print ("diff= "+str(diff))

I got these results:

% python test.py
elaplsedtime1= 2.46446800232
elaplsedtime2= 1.52360200882
diff= -0.617527404173

In other words, the two computations have a 60% performance difference. Is it unexpected?

Upvotes: 1

Views: 88

Answers (1)

bousof
bousof

Reputation: 1251

As it was already answered in the comments, the difference in performance is probably due to indexing.

However I noticed when working with numpy that you get a bad efficiency when calling sum on axis that have a small dimension (3 in your case).

For your test case, you can still get an additional 100% speed-up by replacing the call to the sum function by a matrix product operation with a vector of ones.

Here I added a dimension variable D (D=3 in your case):

import numpy as np
import time

N=100000
i=0
rep=1000

D = 3

x1=np.random.randn(N,4)
x2=np.random.randn(4,N)
G1=np.random.randn(N,4,D)
G2=np.random.randn(4,N,D)
B=np.random.randn(N,D)

ones=np.ones((D,))

starttime=time.time()
for k in range(rep):
    x1[:,i]=(G1[:,i,:] * B[:,:]).sum(axis=1)
elapsedtime1=time.time()-starttime

starttime=time.time()
for k in range (rep):
    x1[:,i]=(G1[:,i,:] * B[:,:]) @ ones
elapsedtime2=time.time()-starttime

print ("elaplsedtime1= "+str(elapsedtime1))
print ("elaplsedtime2= "+str(elapsedtime2))
diff=(elapsedtime2-elapsedtime1)/elapsedtime2
print ("diff12= "+str(diff))

I got these results:

% python3 script.py
elaplsedtime1= 2.2359278202056885
elaplsedtime2= 1.1143040657043457
diff12= -1.006568843300747

Note that the speed-up remains even if the vector ones is created on the fly. However the speed-up decreases when the dimension D increases.

Hope this helps.

Upvotes: 1

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