On Tue, Nov 12, 2013 at 6:17 PM, Julian Taylor < [email protected]> wrote:
> On 12.11.2013 03:17, David Cournapeau wrote: > > Hi there, > > > > I have noticed more and more subtle and hard to track serious bugs in > > numpy and scipy, due to the use of advanced optimization features > > (flags, or gcc intrinsics). > > > > I am wondering whether those are worth it: they compile wrongly under > > quite a few configurations, and it is not always obvious to find the > > cause (case in point: gcc 4.4 with numpy 1.8.0 causes infinite loop in > > scipy.stats, which disappear if I disable the intrinsics in > > numpy/npy_config.h). Maybe they should be disabled by default, and only > > built in if required ? Do we know for sure they bring significant > > improvements ? > > yes, e.g. > > http://yarikoptic.github.io/numpy-vbench/vb_vb_ufunc.html#numpy-add-scalar2-numpy-float32 > http://yarikoptic.github.io/numpy-vbench/vb_vb_ufunc.html#numpy-not-bool > > http://yarikoptic.github.io/numpy-vbench/vb_vb_ufunc.html#numpy-isnan-a-10types > and many more. > > this benchmark runs on a pretty old amd, the improvements are greater on > more modern AMD and Intel cpus. > > > > > While gcc 4.4 is not the most recent compiler, it is not ancient either, > > I can't reproduce any issue with gcc 4.4.7 > So my initial email was wrong, the issue appears with gcc 4.1 and disappears with 4.4. > > Can you narrow it down to a specific intrinsic? they can be enabled and > disabled in set ./numpy/core/setup_common.py > valgrind shows quite a few invalid read in BOOL_ functions when running the scipy or sklearn test suite. BOOL_logical_or is the one that appears the most often. I don't have time to track this down now, but I think it would be good to have at least a system in place to disable the simd intrinsics when building numpy. David _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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