Re: [Numpy-discussion] Changing FFT cache to a bounded LRU cache

> Am 31.05.2016 um 23:36 schrieb Sturla Molden : > > Joseph Martinot-Lagarde wrote: > >> The problem with FFTW is that its license is more restrictive (GPL), and >> because of this may not be suitable everywhere numpy.fft is. > > A lot of us use NumPy linked with MKL or Accelerate, both of whi

Re: [Numpy-discussion] Numexpr-3.0 proposal

> Am 14.02.2016 um 23:19 schrieb Robert McLeod : > > Hello everyone, > > I've done some work on making a new version of Numexpr that would fix some of > the limitations of the original virtual machine with regards to data types > and operation/function count. Basically I re-wrote the Python an

Re: [Numpy-discussion] Linking other libm-Implementation

speed can be gained, a lot more than 25%. > > glibc 2.22 was used running on archlinux. As far as I know openlibm does not > include special vectorized functions. (for reference vectorized operations in > glibc: https://sourceware.org/glibc/wiki/libmvec > <https://so

Re: [Numpy-discussion] Linking other libm-Implementation

> Am 08.02.2016 um 18:36 schrieb Nathaniel Smith : > > On Feb 8, 2016 3:04 AM, "Nils Becker" > wrote: > > > [...] > > Very superficial benchmarks (see below) seem devastating for gnu libm. It > > seems that openlibm (compiled with gcc -mtune=native -O3) performs r

Re: [Numpy-discussion] Introductory mail and GSoc Project "Vector math library integration"

> Am 12.03.2015 um 13:48 schrieb Julian Taylor : > > On 03/12/2015 10:15 AM, Gregor Thalhammer wrote: >> >> Another note, numpy makes it easy to provide new ufuncs, see >> http://docs.scipy.org/doc/numpy-dev/user/c-info.ufunc-tutorial.html >> from a C functio

Re: [Numpy-discussion] Introductory mail and GSoc Project "Vector math library integration"

> Am 11.03.2015 um 23:18 schrieb Dp Docs : > > > > On Wed, Mar 11, 2015 at 10:34 PM, Gregor Thalhammer > mailto:gregor.thalham...@gmail.com>> wrote: > > > > > > On the scipy mailing list I also answered to Amine, who is also interested > > in

Re: [Numpy-discussion] Introductory mail and GSoc Project "Vector math library integration"

> Am 08.03.2015 um 21:47 schrieb Dp Docs : > > Hi all, > I am a CS 3rd Undergrad. Student from an Indian Institute (III T). I > believe I am good in Programming languages like C/C++, Python as I > have already done Some Projects using these language as a part of my > academics. I really like Codi

Re: [Numpy-discussion] Inverted indices

Am 07.08.2014 um 13:59 schrieb Gregor Thalhammer : > > Am 07.08.2014 um 13:16 schrieb Nicolas P. Rougier : > >> >> Hi, >> >> I've a small problem for which I cannot find a solution and I'm quite sure >> there is an obvious one: >> &

Re: [Numpy-discussion] Inverted indices

Am 07.08.2014 um 13:16 schrieb Nicolas P. Rougier : > > Hi, > > I've a small problem for which I cannot find a solution and I'm quite sure > there is an obvious one: > > I've an array Z (any dtype) with some data. > I've a (sorted) array I (of integer, same size as Z) that tells me the index

Re: [Numpy-discussion] GSoC project: draft of proposal

Am 14.03.2014 um 11:00 schrieb Eric Moore : > > > On Friday, March 14, 2014, Gregor Thalhammer > wrote: > > Am 13.03.2014 um 18:35 schrieb Leo Mao : > > > Hi, > > > > Thanks a lot for your advice, Chuck. > > Following your advice, I have modi

Re: [Numpy-discussion] GSoC project: draft of proposal

Am 13.03.2014 um 18:35 schrieb Leo Mao : > Hi, > > Thanks a lot for your advice, Chuck. > Following your advice, I have modified my draft of proposal. (attachment) > I think it still needs more comments so that I can make it better. > > And I found that maybe I can also make some functions rela

Re: [Numpy-discussion] numpy gsoc topic idea: configurable algorithm precision and vector math library integration

Am 03.03.2014 um 20:20 schrieb Julian Taylor : > hi, > > as the numpy gsoc topic page is a little short on options I was thinking > about adding two topics for interested students. But as I have no > experience with gsoc or mentoring and the ideas are not very fleshed out > yet I'd like to ask i

Re: [Numpy-discussion] Custom floating point representation to IEEE 754 double

Am 25.02.2014 um 12:08 schrieb Daniele Nicolodi : > Hello, > > I'm dealing with an instrument that transfers numerical values through > an RS232 port in a custom (?) floating point representation (56 bits, 4 > bits exponent and 52 bits significand). > > Of course I need to convert this format t

Re: [Numpy-discussion] numpythonically getting elements with the minimum sum

Am 28.1.2013 um 23:15 schrieb Lluís: > Hi, > > I have a somewhat convoluted N-dimensional array that contains information of > a > set of experiments. > > The last dimension has as many entries as iterations in the experiment (an > iterative application), and the penultimate dimension has as m

Re: [Numpy-discussion] mkl usage

Am 24.2.2012 um 13:54 schrieb Neal Becker: > Francesc Alted wrote: > >> On Feb 23, 2012, at 2:19 PM, Neal Becker wrote: >> >>> Pauli Virtanen wrote: >>> 23.02.2012 20:44, Francesc Alted kirjoitti: > On Feb 23, 2012, at 1:33 PM, Neal Becker wrote: > >> Is mkl only used for lin

Re: [Numpy-discussion] Owndata flag

Am 16.12.2011 um 11:53 schrieb Fabrice Silva: > Le jeudi 15 décembre 2011 à 18:09 +0100, Gregor Thalhammer a écrit : > >> There is an excellent blog entry from Travis Oliphant, that describes >> how to create a ndarray from existing data without copy: >> http://blog.en

Re: [Numpy-discussion] Owndata flag

Am 15.12.2011 um 17:17 schrieb Fabrice Silva: > How can one arbitrarily assumes that an ndarray owns its data ? > > More explicitly, I have some temporary home-made C structure that holds > a pointer to an array. I prepare (using Cython) an numpy.ndarray using > the PyArray_NewFromDescr function

Re: [Numpy-discussion] polynomial with negative exponents

Am 12.12.2011 um 15:04 schrieb LASAGNA DAVIDE: > Hi, > > I have written a class for polynomials with negative > exponents like: > > p(x) = a0 + a1*x**-1 + ... + an*x**-n > > The code is this one: > > class NegativeExpPolynomial( object ): > def __init__ ( self, coeffs ): > self

Re: [Numpy-discussion] Help in speeding up accumulation in a matrix

Am 29.1.2011 um 22:01 schrieb Nicolas SCHEFFER: > Hi all, > > First email to the list for me, I just want to say how grateful I am > to have python+numpy+ipython etc... for my day to day needs. Great > combination of software. > > Anyway, I've been having this bottleneck in one my algorithms th

Re: [Numpy-discussion] glumpy, fast opengl visualization

2010/1/25 Nicolas Rougier : > > > Hello, > > This is an update about glumpy, a fast-OpenGL based numpy visualization. > I modified the code such that the only dependencies are PyOpenGL and > IPython (for interactive sessions). You will also need matplotlib and > scipy for some demos. > > Sources: h

Re: [Numpy-discussion] Objected-oriented SIMD API for Numpy

2009/10/21 Neal Becker > ... > > I once wrote a module that replaces the built in transcendental > > functions of numpy by optimized versions from Intels vector math > > library. If someone is interested, I can publish it. In my experience it > > was of little use since real world problems are li

Re: [Numpy-discussion] Objected-oriented SIMD API for Numpy

Pauli Virtanen schrieb: > Wed, 21 Oct 2009 14:47:02 +0200, Francesc Alted wrote: > [clip] > >>> Do you have any interest in adding SIMD to some core numpy >>> (transcendental functions). If so, I would try to go back to the >>> problem of runtime SSE detection and loading of optimized shared >>> li

Re: [Numpy-discussion] where are the benefits of ldexp and/or "array times 2"?

dmitrey schrieb: > Hi all, > I expected to have some speedup via using ldexp or multiplying an > array by a power of 2 (doesn't it have to perform a simple shift of > mantissa?), but I don't see the one. > > # Let me also note - > # 1) using b = 2 * ones(N) or b = zeros(N) doesn't yield any speedup

Re: [Numpy-discussion] numpy ufuncs and COREPY - any info?

dmitrey schrieb: > hi all, > has anyone already tried to compare using an ordinary numpy ufunc vs > that one from corepy, first of all I mean the project > http://socghop.appspot.com/student_project/show/google/gsoc2009/python/t124024628235 > > It would be interesting to know what is speedup for (e

Re: [Numpy-discussion] memoization with ndarray arguments

Paul Northug schrieb: > I would like to 'memoize' the objective, derivative and hessian > functions, each taking a 1d double ndarray argument X, that are passed > as arguments to > scipy.optimize.fmin_ncg. > > Each of these 3 functions has calculations in common that are > expensive to compute and

Re: [Numpy-discussion] Build with MKL on linux

Neal Becker schrieb: > Trying to build numpy-1.2.1 with intel mkl 10.1.1.019 on linux F10 x86_64. > > echo $LD_LIBRARY_PATH > /opt/intel/mkl/10.1.1.019/lib/em64t > > > strace -e trace=file python -c 'import numpy; numpy.test()' 2>stuff > Running unit tests for numpy > NumPy version 1.2.1 > NumPy is

Re: [Numpy-discussion] Integer cast problems

Ralph Kube schrieb: > Hi there, > I have a little problem here with array indexing, hope you see the problem. > I use the following loop to calculate some integrals > > ... > 0.145 * 0.005 = 28.996 > N.int32(0.145 * 0.005) = 28 conversion to int truncates, it doesn't round. Try N.int32(

[Numpy-discussion] numexpr and numpy windows binaries built with MKL

Hi all, as Francesc announced, the latest release of Numexpr 1.2 can be built with Intels Math Kernel Library, which gives a BIGbig increase in performance. Now the questions: Could somebody provide binaries for Windows of Numexpr, linked with Intels MKL? I know, there is the license problem.

Re: [Numpy-discussion] Fast threading solution thoughts

Brian Granger schrieb: >> I am curious: would you know what would be different in numpy's case >> compared to matlab array model concerning locks ? Matlab, up to >> recently, only spreads BLAS/LAPACK on multi-cores, but since matlab 7.3 >> (or 7.4), it also uses multicore for mathematical functions

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 a

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

Re: [Numpy-discussion] contiguous regions

John Hunter schrieb: > I frequently want to break a 1D array into regions above and below > some threshold, identifying all such subslices where the contiguous > elements are above the threshold. I have two related implementations > below to illustrate what I am after. The first "crossings" is ra

Re: [Numpy-discussion] Fast and efficient way to convert an array into binary

frank wang schrieb: > Hi, > > I have a large array and I want to convert it into a binary array. For > exampe, y=array([1,2,3]), after the convertion I want the result > array([0,0,0,1,0,0,1,0,0,0,1,1]). Each digit is converted into 4 bits > in this example. In my real problem I want to conver

[Numpy-discussion] failure with numpy.inner

the reason for this behaviour (and fix it). Gregor Thalhammer ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion