On Thu, 4 Feb 2016 21:22:32 +1000 Nick Coghlan <ncoghlan@gmail.com> wrote:
I figured that was independent of the manylinux PEP (since it affects Windows as well), but I'm also curious as to the current status (I found a couple of apparently relevant threads on the NumPy list, but figured it made more sense to just ask for an update rather than trusting my Google-fu)
While I'm not a Numpy maintainer, I don't think you can go much further than SSE2 (which is standard under the x86-64 definition). One factor is support by the kernel. The CentOS 5 kernel doesn't seem to support AVX, so you can't use AVX there even if your processor supports it (as the registers aren't preserved accross context switches). And one design point of manylinux is to support old Linux setups... (*) There are intermediate ISA additions between SSE2 and AVX (additions that don't require OS support), but I'm not sure they help much on compiler-vectorized code as opposed to hand-written assembly. Numpy's pre-compiled loops are typically quite straightforward as far as I've seen. One mitigation is to delegate some operations to an optimized library implementing the appropriate runtime switches: for example linear algebra is delegated by Numpy and Scipy to optimized BLAS and LINPACK libraries (which exist in various implementations such as OpenBLAS or Intel's MKL). (*) (this is an issue a JIT compiler helps circumvent: it generates optimal code for the current CPU ;-)) Regards Antoine.