On May 12, 2009, at 9:47 PM, Mohamed Lrhazi wrote:
> On Wed, May 13, 2009 at 12:31 AM, Chris Colbert
> <[email protected]> wrote:
>> If your making lots of rapid calls to short running functions in the
>> C-library, then you may start to feel the ctypes overhead.
>
> That's what I was afraid to hear..
Hopefully after using Cython a bit, you're fears will quickly go
away :).
> What I was hoping to hear is "Oh
> no, ctypes is all C anyways, and will perform just the same as Cython"
A simple benchmark:
import ctypes
libm = ctypes.cdll.LoadLibrary("libm.dylib") # platform dependent...
def ctypes_sum(N):
lib_sqrt = libm.sqrt
lib_sqrt.argtypes = (ctypes.c_double,)
lib_sqrt.restype = ctypes.c_double
s = 0
for i in range(N):
s += lib_sqrt(i)
return s
%cython
cdef extern from "math.h":
double sqrt(double)
def cython_sum(long N):
cdef int i
cdef double s=0
for i in range(N):
s += sqrt(i)
return s
>>> time ctypes_sum(10**6)
666666166.4588418
Time: CPU 1.13 s, Wall: 1.14 s
time cython_sum(10**6)
666666166.4588418
Time: CPU 0.03 s, Wall: 0.03 s
Of course if you're calling a c function, waiting 1 second for the
result, and then looking at the return value, the overhead won't make
a difference at all. On the other extreme, as in the example above,
the overhead will absolutely kill you.
That being said, ctypes is a very cool module and does have its place.
- Robert
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