WARNING! ANIME SPOILERS AHEAD


  When they were on a mission in the Hidden Mist, the Hidden Mist ninja captured Rin and made her into the 3 tails' Jinchuuriki. Their purpose was for Rin to return to the village, where they'll undo the seal, kill Rin and have the 3 tails rampage on the Hidden Leaf.

Knowing that, Rin jumped in front of Kakashi's Chidori as he charged on a hidden Mist ninja to kill herself and foil their plans.


Obito came a second too late and only saw Rin getting impaled, but later on he too learned the whole story.


   The Hidden Mist shinobi were manipulated by Madara. He was the one who set that plan up then told Obito so it could have awaken his power. Rin had a curse on her heart placed by Madara. Source: Naruto Manga - Chapter 675.


Why did Kakashi kill Rin?
EDIT:

I rerun the code under the Windows 7 x64 (Intel Core i7 930 @ 3.8GHz).

Again, the code is:

from datetime import datetime
import numpy as np

START_TIME = datetime.now()

# one of the following lines is uncommented before execution
#s = np.float64(1)
#s = np.float32(1)
#s = 1.0

for i in range(10000000):
    s = (s + 8) * s % 2399232

print(s)
print('Runtime:', datetime.now() - START_TIME)


The timings are:


float64: 16.1s
float32: 16.1s
float: 3.2s


Now both np floats (either 64 or 32) are 5 times slower than the built-in float. Still, a significant difference. I'm trying to figure out where it comes from.

EDIT:

Thank you for the answers, they help me understand how to deal with this problem.

But I still would like to know the precise reason (based on the source code perhaps) why the code below runs 10 times slow with float64 than with float.

EDIT:

numpy.float64 is 10 times slower than float in arithmetic calculations. It's so bad that even converting to float and back before the calculations makes the program run 3 times faster. Why? Is there anything I can do to fix it?

I want to emphasize that my timings are not due to any of the following:


the function calls
the conversion between numpy and python float
the creation of objects


I updated my code to make it clearer where the problem lies. With the new code, it would seem I see a ten-fold performance hit from using numpy data types:

from datetime import datetime
import numpy as np

START_TIME = datetime.now()

# one of the following lines is uncommented before execution
#s = np.float64(1)
#s = np.float32(1)
#s = 1.0

for i in range(10000000):
    s = (s + 8) * s % 2399232

print(s)
print('Runtime:', datetime.now() - START_TIME)


The timings are:


float64: 34.56s
float32: 35.11s
float: 3.53s


Just for the hell of it, I also tried:

from datetime import datetime
import numpy as np

START_TIME = datetime.now()

s = np.float64(1)
for i in range(10000000):
    s = float(s)
    s = (s + 8) * s % 2399232
    s = np.float64(s)

print(s)
print('Runtime:', datetime.now() - START_TIME)


The execution time is 13.28 s; it's actually 3 times faster to convert the float64 to float and back than to use it as is. Still, the conversion takes its toll, so overall it's more than 3 times slower compared to the pure-python float.

My machine is:


Intel Core 2 Duo T9300 (2.5GHz)
WinXP Professional (32-bit)
ActiveState Python 3.1.3.5
Numpy 1.5.1


END OF EDIT

ORIGINAL QUESTION:

I am getting really weird timings for the following code:

import numpy as np
s = 0
for i in range(10000000):
    s += np.float64(1) # replace with np.float32 and built-in float



built-in float: 4.9 s
float64: 10.5 s
float32: 45.0 s


Why is float64 twice slower than float? And why is float32 5 times slower than float64?

Is there any way to avoid the penalty of using np.float64, and have numpy functions return built-in float instead of float64?

I found that using numpy.float64 is much slower than Python's float, and numpy.float32 is even slower (even though I'm on a 32-bit machine).

numpy.float32 on my 32-bit machine. Therefore, every time I use various numpy functions such as numpy.random.uniform, I convert the result to float32 (so that further operations would be performed at 32-bit precision).

Is there any way to set a single variable somewhere in the program or in the command line, and make all numpy functions return float32 instead of float64?
