Re: [Numpy-discussion] ANN: Numexpr 1.1, an efficient array evaluator

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 wrote: > A Tu

Re: [Numpy-discussion] ANN: Numexpr 1.1, an efficient array evaluator

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

Re: [Numpy-discussion] ANN: Numexpr 1.1, an efficient array evaluator

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

Re: [Numpy-discussion] ANN: Numexpr 1.1, an efficient array evaluator

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 o

Re: [Numpy-discussion] ANN: Numexpr 1.1, an efficient array evaluator

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) S

Re: [Numpy-discussion] ANN: Numexpr 1.1, an efficient array evaluator

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 stra

Re: [Numpy-discussion] ANN: Numexpr 1.1, an efficient array evaluator

A Sunday 18 January 2009, j...@physics.ucf.edu escrigué: > 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 > > > > calculat

Re: [Numpy-discussion] ANN: Numexpr 1.1, an efficient array evaluator

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 pro

Re: [Numpy-discussion] ANN: Numexpr 1.1, an efficient array evaluator

On Sat, Jan 17, 2009 at 4:35 AM, Gregor Thalhammer wrote: > 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 >>> mathematica

Re: [Numpy-discussion] ANN: Numexpr 1.1, an efficient array evaluator

2009/1/16 Gregor Thalhammer : > 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 speedup of 6x for

Re: [Numpy-discussion] ANN: Numexpr 1.1, an efficient array evaluator

Note that Apple has a similar library called vForce: I think these libraries use several techniques and are not neces

Re: [Numpy-discussion] ANN: Numexpr 1.1, an efficient array evaluator

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 >> ext

Re: [Numpy-discussion] ANN: Numexpr 1.1, an efficient array evaluator

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

Re: [Numpy-discussion] ANN: Numexpr 1.1, an efficient array evaluator

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 im

Re: [Numpy-discussion] ANN: Numexpr 1.1, an efficient array evaluator

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

Re: [Numpy-discussion] ANN: Numexpr 1.1, an efficient array evaluator

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 use

Re: [Numpy-discussion] ANN: Numexpr 1.1, an efficient array evaluator

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. > > Plea

Re: [Numpy-discussion] ANN: Numexpr 1.1, an efficient array evaluator

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

[Numpy-discussion] ANN: Numexpr 1.1, an efficient array evaluator

Announcing Numexpr 1.1 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