Re: [Numpy-discussion] Numpy Overhead
Thank you! That is the information I needed. 2017-03-01 0:18 GMT+01:00 Matthew Brett <matthew.br...@gmail.com>: > Hi, > > On Tue, Feb 28, 2017 at 3:04 PM, Sebastian K > <sebastiankas...@googlemail.com> wrote: > > Yes you are right. There is no need to add that line. I deleted it. But > the > > measured heap peak is still the same. > > You're applying the naive matrix multiplication algorithm, which is > ideal for minimizing memory use during the computation, but terrible > for speed-related stuff like keeping values in the CPU cache: > > https://en.wikipedia.org/wiki/Matrix_multiplication_algorithm > > The Numpy version is likely calling into a highly optimized compiled > routine for matrix multiplication, which can load chunks of the > matrices at a time, to speed up computation. If you really need > minimum memory heap usage and don't care about the order of > magnitude(s) slowdown, then you might need to use the naive method, > maybe implemented in Cython / C. > > Cheers, > > Matthew > ___ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Numpy Overhead
Yes you are right. There is no need to add that line. I deleted it. But the measured heap peak is still the same. 2017-03-01 0:00 GMT+01:00 Joseph Fox-Rabinovitz <jfoxrabinov...@gmail.com>: > For one thing, `C = np.empty(shape=(n,n), dtype='float64')` allocates 10^4 > extra elements before being immediately discarded. > > -Joe > > On Tue, Feb 28, 2017 at 5:57 PM, Sebastian K <sebastiankaster@googlemail. > com> wrote: > >> 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 >>> >> >> >> ___ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> https://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > > ___ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Numpy Overhead
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 > ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Numpy Overhead
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) <http://man7.org/linux/man-pages/man3/malloc.3.html>, all products of *nmemb***size* of calloc(3) <http://man7.org/linux/man-pages/man3/calloc.3.html>, all *size* arguments of realloc(3) <http://man7.org/linux/man-pages/man3/realloc.3.html>, *length* arguments of mmap(2) <http://man7.org/linux/man-pages/man2/mmap.2.html>, and *new_size* arguments of mremap(2) <http://man7.org/linux/man-pages/man2/mremap.2.html>. Regards Sebastian On 28 Feb 2017 11:03 p.m., "Benjamin Root" <ben.v.r...@gmail.com> wrote: > You are going to need to provide much more context than that. Overhead > compared to what? And where (io, cpu, etc.)? What are the size of your > arrays, and what sort of operations are you doing? Finally, how much > overhead are you seeing? > > There can be all sorts of reasons for overhead, and some can easily be > mitigated, and others not so much. > > Cheers! > Ben Root > > > On Tue, Feb 28, 2017 at 4:47 PM, Sebastian K < > sebastiankas...@googlemail.com> wrote: > >> Hello everyone, >> >> I'm interested in the numpy project and tried a lot with the numpy array. >> I'm wondering what is actually done that there is so much overhead when I >> call a function in Numpy. What is the reason? >> Thanks in advance. >> >> Regards >> >> Sebastian Kaster >> >> ___ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> https://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > > ___ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Numpy Overhead
Hello everyone, I'm interested in the numpy project and tried a lot with the numpy array. I'm wondering what is actually done that there is so much overhead when I call a function in Numpy. What is the reason? Thanks in advance. Regards Sebastian Kaster ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
