the amazing performance of C++ code does not surprise me: a tenfold improvement of the simple Python/Numpy code can be achieved with weave.inline or Pyrex.
Hence your benchmarks seems to confirm that "weaved" or "pyrexed" code run as fast as C++ compiled one. Moreover, from your numbers, I can tell that compiling numpy with gcc or icc makes no big difference. Am I correct? If yes, let me know if I can add this info to the scipy wiki: I'm preparing an extention to this page http://www.scipy.org/PerformancePython. cheers, lorenzo On 4/17/07, rex <[EMAIL PROTECTED]> wrote:
lorenzo bolla <[EMAIL PROTECTED]> [2007-04-17 00:37]: > as soon as you do it, I'd like to compare them with the benchmarks I posted > here few days ago (compiled with gcc): http://lbolla.wordpress.com/2007/04/11/numerical-computing-matlab-vs-pythonnumpyweave/ Thanks for the link. I haven't built numpy with MKL 9.1 yet, but here are some results running laplace.py using MKL 8.1. The CPU is a Core 2 Duo (currently) overclocked to 2.94 GHz (it will run at 3.52 GHz). Using Python2.5 compiled with icc 9.1, numpy built with MKL 8.1 Doing 100 iterations on a 500x500 grid numeric took 1.53 seconds slow (100 iterations) took 130.02 seconds slow with Psyco (100 iterations) took 107.91 seconds Python compiled with icc takes 85 times longer to run this benchmark than Python/NumPy does. Using Python2.5 compiled with gcc, numpy built with MKL 8.1 Doing 100 iterations on a 500x500 grid numeric took 1.57 seconds slow (100 iterations) took 154.29 seconds slow with Psyco (100 iterations) took 119.88 seconds Python compiled with gcc takes 101 times longer to run this benchmark than Python/NumPy/icc does. The C++ version compiled with gcc 4.1.2 runs in .19 seconds. -rex -- I liked Occam's razor so much I bought the company. _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
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