Re: [Numpy-discussion] building NumPy with Intel CC MKL (solved!)
On 25/01/07, David Cournapeau [EMAIL PROTECTED] wrote: rex wrote: I think it should do much better. A few minutes ago I compiled a C math benchmark with : icc -o3 -parallel -xT and it ran 2.8x as fast as it did when compiled with gcc -o3. In fact, it ran at a little over a gigaflop, which is a higher speed than anyone has reported for this benchmark. Without seeing the benchmark, it would be quite hard to know what's happening. Also, when you are using numpy, you are using python, and for Perhaps compiling python itself with icc might give a useful speedup. Apparently somebody managed this for python 2.3 in 2003: http://mail.python.org/pipermail/c++-sig/2003-October/005824.html --George Nurser. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] building NumPy with Intel CC MKL (solved!)
rex wrote: I've added these lines to .bashrc: source /opt/intel/cc/9.1.042/bin/iccvars.sh export PYTHONPATH=/usr/local/lib/python2.5/site-packages:/usr/lib/python2.5 export INCLUDE=/opt/intel/mkl/8.1/include:$INCLUDE export LD_LIBRARY_PATH=/usr/local/lib:/opt/intel/mkl/8.1/lib/32:$LD_LIBRARY_PATH I don't understand why the 'site-packages' must be included, but without it, numpy is loaded from /usr/lib/python/site-packages. Why does in look in the subdirectories in one case, but not in the other? Oh, well it works. Because SuSE did not configure their Python installation to look in /usr/local/lib/python2.5/site-packages/. -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] building NumPy with Intel CC MKL (solved!)
Hi Rex, Thank you for taking the time to write such a detailed explanation. If only the documentation were so detailed... Now that you've gone through your odyssey trying to numpy/scipy w/ this particular combo (SuSE/MKL/IntelCC), now would be a great time to whip up wiki page ... you know .. for the documentation ;-) So the rpm version only takes ~17% longer to run this program. I'm surprised that there isn't a larger difference. Perhaps there will be in a different type of program. BTW, the cpu is an Intel e6600 Core 2 Duo overclocked to 3.06 GHz (it will run reliably at 3.24 GHz). That's not so bad, though, is it? I'd also be interested in seeing some more benchmarks though .. I wonder if there is a standard benchmarking suite somewhere .. Congrats on completing the gauntlet, -steve ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] building NumPy with Intel CC MKL (solved!)
Steve Lianoglou wrote: Hi Rex, Thank you for taking the time to write such a detailed explanation. If only the documentation were so detailed... Now that you've gone through your odyssey trying to numpy/scipy w/ this particular combo (SuSE/MKL/IntelCC), now would be a great time to whip up wiki page ... you know .. for the documentation ;-) So the rpm version only takes ~17% longer to run this program. I'm surprised that there isn't a larger difference. Perhaps there will be in a different type of program. BTW, the cpu is an Intel e6600 Core 2 Duo overclocked to 3.06 GHz (it will run reliably at 3.24 GHz). That's not so bad, though, is it? I'd also be interested in seeing some more benchmarks though .. I wonder if there is a standard benchmarking suite somewhere .. The code used for this benchmark uses only two few functions: poisson and sum, and I wouldn't be suprised that a lot of code is spent in python (vs in the core C functions), where the intel compiler doesn't make a big difference. Does this code uses the MKL at all ? The MKL gives an optimized fft and BLAS/LAPACK, right ? David ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] building NumPy with Intel CC MKL (solved!)
Steve Lianoglou [EMAIL PROTECTED] [2007-01-24 20:06]: Now that you've gone through your odyssey trying to numpy/scipy w/ this particular combo (SuSE/MKL/IntelCC), now would be a great time to whip up wiki page ... you know .. for the documentation ;-) Yes, I should do that, but I want to optimize the compiler flags first, and try to get SciPy to build. So the rpm version only takes ~17% longer to run this program. I'm surprised that there isn't a larger difference. Perhaps there will be in a different type of program. BTW, the cpu is an Intel e6600 Core 2 Duo overclocked to 3.06 GHz (it will run reliably at 3.24 GHz). That's not so bad, though, is it? I'd also be interested in seeing some more benchmarks though .. I wonder if there is a standard benchmarking suite somewhere .. I think it should do much better. A few minutes ago I compiled a C math benchmark with : icc -o3 -parallel -xT and it ran 2.8x as fast as it did when compiled with gcc -o3. In fact, it ran at a little over a gigaflop, which is a higher speed than anyone has reported for this benchmark. Congrats on completing the gauntlet, Thank. It's the 2nd time. I eventually succeed with an earlier version as well, thanks to Travis. -rex ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] building NumPy with Intel CC MKL (solved!)
rex wrote: I think it should do much better. A few minutes ago I compiled a C math benchmark with : icc -o3 -parallel -xT and it ran 2.8x as fast as it did when compiled with gcc -o3. In fact, it ran at a little over a gigaflop, which is a higher speed than anyone has reported for this benchmark. Without seeing the benchmark, it would be quite hard to know what's happening. Also, when you are using numpy, you are using python, and for some cases, it can be really easy to slow things down because you are doing something wrong (an example is using non contiguous arrays without knowing it; I got caught often when translating some matlab code to numpy); also the numeric code in numpy *may* be written in a way that icc cannot optimize as well as pure C code. All this is pure speculations, without seeing and running/profiling the actual code David ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
