Yes it is true the execution time is much faster with the numpy function. The Code for numpy version:
def createMatrix(n): Matrix = np.empty(shape=(n,n), dtype='float64') for x in range(n): for y in range(n): Matrix[x, y] = 0.1 + ((x*y)%1000)/1000.0 return Matrix if __name__ == '__main__': n = getDimension() if n > 0: A = createMatrix(n) B = createMatrix(n) C = np.empty(shape=(n,n), dtype='float64') C = np.dot(A,B) #print(C) In the pure python version I am just implementing the multiplication with three for-loops. Measured data with libmemusage: dimension of matrix: 100x100 heap peak pure python3: 1060565 heap peakt numpy function: 4917180 2017-02-28 23:17 GMT+01:00 Matthew Brett <matthew.br...@gmail.com>: > Hi, > > On Tue, Feb 28, 2017 at 2:12 PM, Sebastian K > <sebastiankas...@googlemail.com> wrote: > > Thank you for your answer. > > For example a very simple algorithm is a matrix multiplication. I can see > > that the heap peak is much higher for the numpy version in comparison to > a > > pure python 3 implementation. > > The heap is measured with the libmemusage from libc: > > > > heap peak > > Maximum of all size arguments of malloc(3), all > products > > of nmemb*size of calloc(3), all size arguments of > > realloc(3), length arguments of mmap(2), and new_size > > arguments of mremap(2). > > Could you post the exact code you're comparing? > > I think you'll find that a naive Python 3 matrix multiplication method > is much, much slower than the same thing with Numpy, with arrays of > any reasonable size. > > Cheers, > > Matthew > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion >
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