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?


Question: What is the difference between "Mongolian Grill" and "Hibachi"?
Answer: I know this is an old post but I'd like to add something that I was told by the owner of a Mongolian resturant. He told me the origin of what is now called Mongolian BBQ was the cooking methods of the Mongolian soldiers (read: Genghis Khan's army) who fought and traveled for long periods. They would communally gather food - meats, fish, vegatables, etc., and then individually cook their own meals in their helmets, over an open campfire. They carried oils and sauces along with them and they were considered prized possessions.

I can easily imagine this to be true and that this tradition would evolve into a family practice in a home, in a larger "wok" device, and eventully into a resturant setting. Somewhere along the line, the wok was inverted to make the cooking of multiple meals possible.