On 11/07/2012 03:30 PM, Neal Becker wrote: > David Cournapeau wrote: > >> On Wed, Nov 7, 2012 at 1:56 PM, Neal Becker <[email protected]> wrote: >>> David Cournapeau wrote: >>> >>>> On Wed, Nov 7, 2012 at 12:35 PM, Neal Becker <[email protected]> wrote: >>>>> I'm trying to do a bit of benchmarking to see if amd libm/acml will help >>>>> me. >>>>> >>>>> I got an idea that instead of building all of numpy/scipy and all of my >>>>> custom modules against these libraries, I could simply use: >>>>> >>>>> >>> > LD_PRELOAD=/opt/amdlibm-3.0.2/lib/dynamic/libamdlibm.so:/opt/acml5.2.0/gfortran64/lib/libacml.so >>>>> <my program here> >>>>> >>>>> I'm hoping that both numpy and my own dll's then will take advantage of >>>>> these libraries. >>>>> >>>>> Do you think this will work? >>>> >>>> Quite unlikely depending on your configuration, because those >>>> libraries are rarely if ever ABI compatible (that's why it is such a >>>> pain to support). >>>> >>>> David >>> >>> When you say quite unlikely (to work), you mean >>> >>> a) unlikely that libm/acml will be used to resolve symbols in numpy/dlls at >>> runtime (e.g., exp)? >>> >>> or >>> >>> b) program may produce wrong results and/or crash ? >> >> Both, actually. That's not something I would use myself. Did you try >> openblas ? It is open source, simple to build, and is pretty fast, >> >> David > > In my current work, probably the largest bottlenecks are 'max*', which are > > log (\sum e^(x_i))
numexpr with Intel VML is the solution I know of that doesn't require you to dig into compiling C code yourself. Did you look into that or is using Intel VML/MKL not an option? Fast exps depend on the CPU evaluating many exp's at the same time (both explicit through vector registers, and implicit through pipelining); even if you get what you try to work (which is unlikely I think) the approach is inherently slow, since just passing a single number at the time through the "exp" function can't be efficient. Dag Sverre _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
