Julian Taylor <jtaylor.debian <at> googlemail.com> writes: > can you show the code that is slow in numpy? > which version of gcc and libc are you using? > with gcc 4.8 it uses the glibc 2.17 sin/cos with fast-math, so there > should be no difference.
In trying to write some simple code to demonstrate it, I realised it was weirdly more complicated than I thought. Previously I had been comparing numpy against weave on a complicated expression, namely a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau). Doing that with weave and no -ffast-math took the same time as numpy approximately, but with weave and -ffast-math it was about 30x faster. Here only a and v are arrays. Since numpy and weave with no -ffast-math took about the same time I assumed it wasn't memory bound but to do with the -ffast-math. Here's the demo code (you might need to comment a couple of lines out if you want to actually run it, since it also tests a couple of things that depend on a library): http://bit.ly/IziH8H However, when I did a simple example that just computed y=sin(x) for arrays x and y, I found that numpy and weave without -ffast-math took about the same time, but weave with -ffast-math was significantly slower than numpy! My take home message from this: optimisation is weird. Could it be that -ffast-math and -O3 allow SSE instructions and that there is some overhead to this that makes it worth it for a complex expression but not for a simple expression? Here's the code for the simple example (doesn't have any dependencies): http://bit.ly/18wdCKY For reference, I'm on a newish 64 bit windows machine running 32 bit Python 2.7.3, gcc version 4.5.2, numpy 1.8.0 installed from binaries. Dan _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
