Why is np.dot much slower the first time it is called in a python session?
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I am trying to do series of big np.dot(a,x) operations, and the first one seems to take far longer than subsequent calls. In my problem, a is tall [n x 2] and x is [2 x 1]. My big matrix a is constant, it is just x that is changing. Here is a MWE:
import numpy as np
@profile
def do_work(a,x):
tmp = np.dot(a,x)
return tmp
@profile
def do_work_iter(a,x):
tmp = np.dot(a,x)
return tmp
if __name__=="__main__":
n = 50000
a = np.random.randn(n,2)
x = np.random.randn(2,1)
#
tmp = do_work(a,x)
#
niter = 100
for i in range(niter):
x = np.random.randn(2,1)
tmp = do_work_iter(a,x)
Using line_profiler, I get .155 s/call for the first call to np.dot and .00013 s/call for the subsequent ones. Is there some setup/error checking that numpy is doing the first time here? Is there a way I can bypass any of it? Or is there some kind of searching for blas functions that is taking all of the time?
I also ran profile and it gives the following:
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 1.514 1.514 {built-in method builtins.exec}
1 0.000 0.000 1.514 1.514 <string>:1(<module>)
1 0.000 0.000 1.514 1.514 speed_small.py:15(run)
101 1.503 0.015 1.503 0.015 {built-in method numpy.core.multiarray.dot}
1 0.000 0.000 1.491 1.491 speed_small.py:5(do_work)
100 0.000 0.000 0.012 0.000 speed_small.py:10(do_work_iter)
so numpy.core.multiarray.dot is taking all of the time, it doesn't give much insight about anything further down the stack.
I am on Python 3.6 from Anaconda and have mkl installed (Windows 7).
python performance numpy
asked Nov 21 '18 at 20:55
Isaac AsherIsaac Asher
384
add a comment |
I am trying to do series of big np.dot(a,x) operations, and the first one seems to take far longer than subsequent calls. In my problem, a is tall [n x 2] and x is [2 x 1]. My big matrix a is constant, it is just x that is changing. Here is a MWE:
import numpy as np
@profile
def do_work(a,x):
tmp = np.dot(a,x)
return tmp
@profile
def do_work_iter(a,x):
tmp = np.dot(a,x)
return tmp
if __name__=="__main__":
n = 50000
a = np.random.randn(n,2)
x = np.random.randn(2,1)
#
tmp = do_work(a,x)
#
niter = 100
for i in range(niter):
x = np.random.randn(2,1)
tmp = do_work_iter(a,x)
Using line_profiler, I get .155 s/call for the first call to np.dot and .00013 s/call for the subsequent ones. Is there some setup/error checking that numpy is doing the first time here? Is there a way I can bypass any of it? Or is there some kind of searching for blas functions that is taking all of the time?
I also ran profile and it gives the following:
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 1.514 1.514 {built-in method builtins.exec}
1 0.000 0.000 1.514 1.514 <string>:1(<module>)
1 0.000 0.000 1.514 1.514 speed_small.py:15(run)
101 1.503 0.015 1.503 0.015 {built-in method numpy.core.multiarray.dot}
1 0.000 0.000 1.491 1.491 speed_small.py:5(do_work)
100 0.000 0.000 0.012 0.000 speed_small.py:10(do_work_iter)
so numpy.core.multiarray.dot is taking all of the time, it doesn't give much insight about anything further down the stack.
I am on Python 3.6 from Anaconda and have mkl installed (Windows 7).
python performance numpy
asked Nov 21 '18 at 20:55
Isaac AsherIsaac Asher
384
I useline_profilerbut my guess would be that you're incurring theimportoverhead the first time. After that, The module is cached
– roganjosh
Nov 21 '18 at 20:58
@roganjosh Isn't the module included before the script comes toif __name__ == '__main__':part ?
– eozd
Nov 21 '18 at 20:59
It's an external module and nothing in your code suggests you're using it (EDIT: that's a lie, you're using@profiledecorator so I'll rethink) I don't know exactly what timings get bundled into the main library timings.
– roganjosh
Nov 21 '18 at 21:02
1
Loading of BLAS?
– Ante
Nov 23 '18 at 12:59
add a comment |
I am trying to do series of big np.dot(a,x) operations, and the first one seems to take far longer than subsequent calls. In my problem, a is tall [n x 2] and x is [2 x 1]. My big matrix a is constant, it is just x that is changing. Here is a MWE:
import numpy as np
@profile
def do_work(a,x):
tmp = np.dot(a,x)
return tmp
@profile
def do_work_iter(a,x):
tmp = np.dot(a,x)
return tmp
if __name__=="__main__":
n = 50000
a = np.random.randn(n,2)
x = np.random.randn(2,1)
#
tmp = do_work(a,x)
#
niter = 100
for i in range(niter):
x = np.random.randn(2,1)
tmp = do_work_iter(a,x)
Using line_profiler, I get .155 s/call for the first call to np.dot and .00013 s/call for the subsequent ones. Is there some setup/error checking that numpy is doing the first time here? Is there a way I can bypass any of it? Or is there some kind of searching for blas functions that is taking all of the time?
I also ran profile and it gives the following:
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 1.514 1.514 {built-in method builtins.exec}
1 0.000 0.000 1.514 1.514 <string>:1(<module>)
1 0.000 0.000 1.514 1.514 speed_small.py:15(run)
101 1.503 0.015 1.503 0.015 {built-in method numpy.core.multiarray.dot}
1 0.000 0.000 1.491 1.491 speed_small.py:5(do_work)
100 0.000 0.000 0.012 0.000 speed_small.py:10(do_work_iter)
so numpy.core.multiarray.dot is taking all of the time, it doesn't give much insight about anything further down the stack.
I am on Python 3.6 from Anaconda and have mkl installed (Windows 7).
python performance numpy
asked Nov 21 '18 at 20:55
Isaac AsherIsaac Asher
384
I am trying to do series of big np.dot(a,x) operations, and the first one seems to take far longer than subsequent calls. In my problem, a is tall [n x 2] and x is [2 x 1]. My big matrix a is constant, it is just x that is changing. Here is a MWE:
import numpy as np
@profile
def do_work(a,x):
tmp = np.dot(a,x)
return tmp
@profile
def do_work_iter(a,x):
tmp = np.dot(a,x)
return tmp
if __name__=="__main__":
n = 50000
a = np.random.randn(n,2)
x = np.random.randn(2,1)
#
tmp = do_work(a,x)
#
niter = 100
for i in range(niter):
x = np.random.randn(2,1)
tmp = do_work_iter(a,x)
Using line_profiler, I get .155 s/call for the first call to np.dot and .00013 s/call for the subsequent ones. Is there some setup/error checking that numpy is doing the first time here? Is there a way I can bypass any of it? Or is there some kind of searching for blas functions that is taking all of the time?
I also ran profile and it gives the following:
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 1.514 1.514 {built-in method builtins.exec}
1 0.000 0.000 1.514 1.514 <string>:1(<module>)
1 0.000 0.000 1.514 1.514 speed_small.py:15(run)
101 1.503 0.015 1.503 0.015 {built-in method numpy.core.multiarray.dot}
1 0.000 0.000 1.491 1.491 speed_small.py:5(do_work)
100 0.000 0.000 0.012 0.000 speed_small.py:10(do_work_iter)
so numpy.core.multiarray.dot is taking all of the time, it doesn't give much insight about anything further down the stack.
I am on Python 3.6 from Anaconda and have mkl installed (Windows 7).
python performance numpy
python performance numpy
asked Nov 21 '18 at 20:55
Isaac AsherIsaac Asher
384
asked Nov 21 '18 at 20:55
Isaac AsherIsaac Asher
384
asked Nov 21 '18 at 20:55
Isaac AsherIsaac Asher
384
asked Nov 21 '18 at 20:55
Isaac AsherIsaac Asher
384
asked Nov 21 '18 at 20:55
Isaac AsherIsaac Asher
384
384
I useline_profilerbut my guess would be that you're incurring theimportoverhead the first time. After that, The module is cached
– roganjosh
Nov 21 '18 at 20:58
@roganjosh Isn't the module included before the script comes toif __name__ == '__main__':part ?
– eozd
Nov 21 '18 at 20:59
It's an external module and nothing in your code suggests you're using it (EDIT: that's a lie, you're using@profiledecorator so I'll rethink) I don't know exactly what timings get bundled into the main library timings.
– roganjosh
Nov 21 '18 at 21:02
1
Loading of BLAS?
– Ante
Nov 23 '18 at 12:59
add a comment |
I useline_profilerbut my guess would be that you're incurring theimportoverhead the first time. After that, The module is cached
– roganjosh
Nov 21 '18 at 20:58
@roganjosh Isn't the module included before the script comes toif __name__ == '__main__':part ?
– eozd
Nov 21 '18 at 20:59
It's an external module and nothing in your code suggests you're using it (EDIT: that's a lie, you're using@profiledecorator so I'll rethink) I don't know exactly what timings get bundled into the main library timings.
– roganjosh
Nov 21 '18 at 21:02
1
Loading of BLAS?
– Ante
Nov 23 '18 at 12:59
I use
line_profiler but my guess would be that you're incurring the import overhead the first time. After that, The module is cached– roganjosh
Nov 21 '18 at 20:58
I use
line_profiler but my guess would be that you're incurring the import overhead the first time. After that, The module is cached– roganjosh
Nov 21 '18 at 20:58
@roganjosh Isn't the module included before the script comes to
if __name__ == '__main__': part ?– eozd
Nov 21 '18 at 20:59
@roganjosh Isn't the module included before the script comes to
if __name__ == '__main__': part ?– eozd
Nov 21 '18 at 20:59
It's an external module and nothing in your code suggests you're using it (EDIT: that's a lie, you're using
@profile decorator so I'll rethink) I don't know exactly what timings get bundled into the main library timings.– roganjosh
Nov 21 '18 at 21:02
It's an external module and nothing in your code suggests you're using it (EDIT: that's a lie, you're using
@profile decorator so I'll rethink) I don't know exactly what timings get bundled into the main library timings.– roganjosh
Nov 21 '18 at 21:02
1
1
Loading of BLAS?
– Ante
Nov 23 '18 at 12:59
Loading of BLAS?
– Ante
Nov 23 '18 at 12:59
add a comment |
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I use
line_profilerbut my guess would be that you're incurring theimportoverhead the first time. After that, The module is cached– roganjosh
Nov 21 '18 at 20:58
@roganjosh Isn't the module included before the script comes to
if __name__ == '__main__':part ?– eozd
Nov 21 '18 at 20:59
It's an external module and nothing in your code suggests you're using it (EDIT: that's a lie, you're using
@profiledecorator so I'll rethink) I don't know exactly what timings get bundled into the main library timings.– roganjosh
Nov 21 '18 at 21:02
1
Loading of BLAS?
– Ante
Nov 23 '18 at 12:59