[Numpy-discussion] Numpy Benchmarking (original) (raw)

Keith Goodman kwgoodman at gmail.com
Tue Jun 27 22🔞51 EDT 2006

On 6/27/06, Keith Goodman <kwgoodman at gmail.com> wrote:

On 6/27/06, Travis Oliphant <oliphant.travis at ieee.org> wrote:

> The numpy.dual library exists so you can use the SciPy calls if the > person has SciPy installed or the NumPy ones otherwise. It exists > precisely for the purpose of seamlessly taking advantage of > algorithms/interfaces that exist in NumPy but are improved in SciPy. That sounds very interesting. It would make a great addition to the scipy performance page: http://scipy.org/PerformanceTips So if I need any of the following functions I should import them from scipy or from numpy.dual? And all of them are faster? fft ifft fftn ifftn fft2 ifft2 norm inv svd solve det eig eigvals eigh eigvalsh lstsq pinv cholesky http://svn.scipy.org/svn/numpy/trunk/numpy/dual.py

Scipy computes the inverse of a matrix faster than numpy (except if the dimensions of x are small). But scipy is slower than numpy for eigh (I only checked for symmetric positive definite matrices):

from numpy import asmatrix, randn from numpy.linalg import eigh as Neigh from scipy.linalg import eigh as Seigh import time

def test(N):

x = asmatrix(randn(N,2*N)) x = x * x.T

t0 = time.time() eigval, eigvec = Neigh(x) t1 = time.time()

t2 = time.time() eigval, eigvec = Seigh(x) t3 = time.time()

print 'NumPy:', t1-t0, 'seconds' print 'SciPy:', t3-t2, 'seconds'

dual.test(10) NumPy: 0.000217914581299 seconds SciPy: 0.000226020812988 seconds

dual.test(100) NumPy: 0.0123109817505 seconds SciPy: 0.0321230888367 seconds

dual.test(200) NumPy: 0.0793058872223 seconds SciPy: 0.082535982132 seconds

dual.test(500) NumPy: 0.59161400795 seconds SciPy: 1.41600894928 seconds

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