To John:
Did you try larger arrays/tuples? I would guess that makes a significant
difference.
No I didn't, due to the fact that these values are coordinates in 3D (x,y,z).
In fact I work with a list/array/tuple of arrays with 10 to 1M of elements
or more.
What I need to do is to calculate
-Original Message-
From: numpy-discussion-boun...@scipy.org [mailto:numpy-discussion-
boun...@scipy.org] On Behalf Of David Cournapeau
Sent: Montag, 10. Januar 2011 10:15
To: Discussion of Numerical Python
Subject: Re: [Numpy-discussion] speed of numpy.ndarray compared
A Sunday 09 January 2011 23:45:02 Mark Wiebe escrigué:
As a benchmark of C-based iterator usage and to make it work properly
in a multi-threaded context, I've updated numexpr to use the new
iterator. In addition to some performance improvements, this also
made it easy to add optional out= and
Hey,
On Mon, 2011-01-10 at 08:09 +, EMMEL Thomas wrote:
#~
def bruteForceSearch(points, point):
minpt = min([(vec2Norm(pt, point), pt, i)
for i, pt in enumerate(points)], key=itemgetter(0))
A Monday 10 January 2011 11:05:27 Francesc Alted escrigué:
Also, I'd like to try out the new thread scheduling that you
suggested to me privately (i.e. T0T1T0T1... vs T0T0...T1T1...).
I've just implemented the new partition schema in numexpr
(T0T0...T1T1..., being the original T0T1T0T1...).
Hi all,
I have this problem: Given some point draw a circle centered in this
point with radius r. I'm doing that using numpy this way (Snippet code
from here [1]):
# Create the initial black and white image
import numpy as np
from scipy import ndimage
a = np.zeros((512, 512)).astype(uint8)
Hey back...
#~
~
~~~
def bruteForceSearch(points, point):
minpt = min([(vec2Norm(pt, point), pt, i)
for i, pt in enumerate(points)], key=itemgetter(0))
return sqrt(minpt[0]), minpt[1],
Hi,
On 01/10/2011 09:09 AM, EMMEL Thomas wrote:
No I didn't, due to the fact that these values are coordinates in 3D (x,y,z).
In fact I work with a list/array/tuple of arrays with 10 to 1M of
elements or more.
What I need to do is to calculate the distance of each of these elements
Hi,
I noticed that I can index into a dtype when I take an element
of a rank-1 array but not if I make a rank-0 array directly. This seems
inconsistent. A bug?
Nils
In [76]: np.version.version
Out[76]: '1.5.1'
In [78]: dt = np.dtype([('x', 'f8'), ('y', 'f8')])
In [80]: a_rank_1 =
Hi,
Spatial hashes are the common solution.
Another common optimization is using the distance squared for
collision detection. Since you do not need the expensive sqrt for
this calc.
cu.
On Mon, Jan 10, 2011 at 3:25 PM, Pascal pascal...@parois.net wrote:
Hi,
On 01/10/2011 09:09 AM, EMMEL
On Mon, Jan 10, 2011 at 2:05 AM, Francesc Alted fal...@pytables.org wrote:
snip
Your patch looks mostly fine to my eyes; good job! Unfortunately, I've
been unable to compile your new_iterator branch of NumPy:
numpy/core/src/multiarray/multiarraymodule.c:45:33: fatal error:
On Mon, Jan 10, 2011 at 10:08, Nils Becker n.bec...@amolf.nl wrote:
Hi,
I noticed that I can index into a dtype when I take an element
of a rank-1 array but not if I make a rank-0 array directly. This seems
inconsistent. A bug?
Not a bug. Since there is no axis, you cannot use integers to
A Monday 10 January 2011 17:54:16 Mark Wiebe escrigué:
Apparently, you forgot to add the new_iterator_pywrap.h file.
Oops, that's added now.
Excellent. It works now.
The aligned case should just be a matter of conditionally removing
the NPY_ITER_ALIGNED flag in two places.
Wow, the
Robert,
your answer does work: after indexing with () I can then further index
into the datatype.
In [115]: a_rank_0[()][0]
Out[115]: 0.0
I guess I just found the fact confusing that a_rank_1[0] and a_rank_0
compare and print equal but behave differently under indexing.
More precisely if I do
On Mon, Jan 10, 2011 at 9:47 AM, Francesc Alted fal...@pytables.org wrote:
snip
so, the new code is just 5% slower. I suppose that removing the
NPY_ITER_ALIGNED flag would give us a bit more performance, but that's
great as it is now. How did you do that? Your new_iter branch in NumPy
On Mon, Jan 10, 2011 at 12:15, Nils Becker n.bec...@amolf.nl wrote:
Robert,
your answer does work: after indexing with () I can then further index
into the datatype.
In [115]: a_rank_0[()][0]
Out[115]: 0.0
I guess I just found the fact confusing that a_rank_1[0] and a_rank_0
compare and
I'm a bit curious why the jump from 1 to 2 threads is scaling so poorly.
Your timings have improvement factors of 1.85, 1.68, 1.64, and 1.79. Since
the computation is trivial data parallelism, and I believe it's still pretty
far off the memory bandwidth limit, I would expect a speedup of 1.95 or
On Mon, Jan 10, 2011 at 11:35 AM, Mark Wiebe mwwi...@gmail.com wrote:
I'm a bit curious why the jump from 1 to 2 threads is scaling so poorly.
Your timings have improvement factors of 1.85, 1.68, 1.64, and 1.79. Since
the computation is trivial data parallelism, and I believe it's still
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