A Tuesday 20 January 2009, Andrew Collette escrigué:
Works much, much better with the current svn version. :) Numexpr now
outperforms everything except the simple technique, and then only
for small data sets.
Correct. This is because of the cost of parsing the expression and
initializing the
Hi,
I get identical results for both shapes now; I manually removed the
numexpr-1.1.1.dev-py2.5-linux-i686.egg folder in site-packages and
reinstalled. I suppose there must have been a stale set of files
somewhere.
Andrew Collette
On Wed, Jan 21, 2009 at 3:41 AM, Francesc Alted
A Tuesday 20 January 2009, Andrew Collette escrigué:
Hi Francesc,
Looks like a cool project! However, I'm not able to achieve the
advertised speed-ups. I wrote a simple script to try three
approaches to this kind of problem:
1) Native Python code (i.e. will try to do everything at once
Works much, much better with the current svn version. :) Numexpr now
outperforms everything except the simple technique, and then only
for small data sets.
Along the lines you mentioned I noticed that simply changing from a
shape of (100*100*100,) to (100, 100, 100) results in nearly a factor
of
Thanks! I think this will help the package attract a lot of users.
A couple of housekeeping things:
on http://code.google.com/p/numexpr:
What it is? - What is it? or What it is (no question mark)
on http://code.google.com/p/numexpr/wiki/Overview:
The last example got incorporated as
Hi Francesc,
Looks like a cool project! However, I'm not able to achieve the
advertised speed-ups. I wrote a simple script to try three approaches
to this kind of problem:
1) Native Python code (i.e. will try to do everything at once using temp arrays)
2) Straightforward numexpr evaluation
3)
Francesc Alted wrote:
Numexpr is a fast numerical expression evaluator for NumPy. With
it, expressions that operate on arrays (like 3*a+4*b) are
accelerated and use less memory than doing the same calculation in
Python.
Please pardon my ignorance as I know this project has been
Francesc Alted schrieb:
Numexpr is a fast numerical expression evaluator for NumPy. With it,
expressions that operate on arrays (like 3*a+4*b) are accelerated
and use less memory than doing the same calculation in Python.
The expected speed-ups for Numexpr respect to NumPy are between 0.95x
A Friday 16 January 2009, j...@physics.ucf.edu escrigué:
Hi Francesc,
Numexpr is a fast numerical expression evaluator for NumPy. With
it, expressions that operate on arrays (like 3*a+4*b) are
accelerated and use less memory than doing the same calculation in
Python.
Please pardon my
Hi Francesc,
this is a wonderful project ! I was just wondering if you would /
could support single precision float arrays ?
In 3+D image analysis we generally don't have enough memory to effort
double precision; and we could save our selves lots of extra C coding
(or Cython) coding of we could
A Friday 16 January 2009, Gregor Thalhammer escrigué:
I also gave a try to the vector math library (VML), contained in
Intel's Math Kernel Library. This offers a fast implementation of
mathematical functions, operating on array. First I implemented a C
extension, providing new ufuncs. This
A Friday 16 January 2009, Sebastian Haase escrigué:
Hi Francesc,
this is a wonderful project ! I was just wondering if you would /
could support single precision float arrays ?
As I said before, it is doable, but I don't know if I will have time
enough to implement this myself.
In 3+D image
Francesc Alted wrote:
A Friday 16 January 2009, j...@physics.ucf.edu escrigué:
Right
now, I'm not quite sure whether the problem you are solving is merely
the case of expressions-in-strings, and there is no advantage for
expressions-in-code, or whether your expressions-in-strings are
faster
Francesc Alted schrieb:
A Friday 16 January 2009, Gregor Thalhammer escrigué:
I also gave a try to the vector math library (VML), contained in
Intel's Math Kernel Library. This offers a fast implementation of
mathematical functions, operating on array. First I implemented a C
extension,
Note that Apple has a similar library called vForce:
http://developer.apple.com/ReleaseNotes/Performance/RN-vecLib/index.html
http://developer.apple.com/documentation/Performance/Conceptual/vecLib/Reference/reference.html
I think these libraries use several techniques and are not
2009/1/16 Gregor Thalhammer gregor.thalham...@gmail.com:
Francesc Alted schrieb:
Wow, pretty nice speed-ups indeed! In fact I was thinking in including
support for threading in Numexpr (I don't think it would be too
difficult, but let's see). BTW, do you know how VML is able to achieve
a
16 matches
Mail list logo