On Dec 10, 2008, at 2:24 AM, Jelle Feringa wrote:
>> Thing is that currently I'm working on sampling b-spline surfaces,
>> and
> compute the curvature of such a surface.
> Basically that comes down to making a 500*500 grid of curvature
> samples.
> Computing the curvature is really quick ( call to a SWIG wrapped
> object ).
> My code looks like:
>
> for a in xrange(1, 500):
> for b in xrange(1, 500):
> do_curvature()
Just to follow up on this, I did some timings.
%cython
def do_loop_c(f, int n, int m):
cdef int i, j
for i in range(n):
for j in range(m):
f()
cdef foo_c():
pass
def do_loop_c_call(int n, int m):
cdef int i, j
for i in range(n):
for j in range(m):
foo_c()
%python
def foo():
"""A "fast" function to call."""
pass
def do_loop_py(f, n, m):
for i in xrange(n):
for j in xrange(m):
f()
def do_loop_empty(f, n, m):
for i in xrange(n):
for j in xrange(m):
pass
sage: time do_loop_py(foo, 5000, 500)
CPU time: 0.56 s, Wall time: 0.56 s
sage: time do_loop_empty(foo, 5000, 500)
CPU time: 0.10 s, Wall time: 0.10 s
sage: time do_loop_c(foo, 5000, 500)
CPU time: 0.34 s, Wall time: 0.34 s
sage: time do_loop_c_call(5000, 500)
CPU time: 0.00 s, Wall time: 0.00 s
sage: time do_loop_c_call(5000, 50000) # 100x anything above
CPU time: 0.22 s, Wall time: 0.22 s
The moral of the story is that the loop overhead can be reduced to
essentially a clock cycle or two per iteration (nanoseconds) but the
cost to call a Python function is several of orders of magnitude more
that that.
- Robert
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