On Mon, Feb 22, 2021 at 1:44 PM Matti Picus <matti.pi...@gmail.com> wrote:
> On 2/22/21 2:13 PM, ChunLin Fang wrote: > > Hi all, > Whether you're running apps on your phone or the world's fastest > supercomputer, you're most likely running ARM. Many major events have > occurred related to ARM archtecture: > > - Apple may have done the most to make ARM relatively relevant in > popular culture with its new ARM-based M1 processor. > - Amazon Web Services launched its Graviton2 processors based on the > Arm architecture , which promise up to 40% better performance from > comparable x86-based instances for 20% less. > - Microsoft currently uses Arm-based chips from Qualcomm in some of > its Surface PCs. > - Huawei unveiled a new chipset called the Kunpeng based on ARM, > designed to go into its new TaiShan servers, in a bid to boost its nascent > cloud business. > > So It's obvious that ARM will become more and more popular in the > future, Since Intel MKL has provide good accelerate support for X86-based > chips, Huawei also published KML_BLAS > <https://kunpeng.huawei.com/en/#/developer/devkit/library>(kunpeng math > library blas) that can make full advantage of ARM-based chips, KML_BLAS is > a mathematical library for basic linear algebra operations. it provides > three levels of high-performance vector operations: vector-vector > operations, vector-matrix operations, and matrix-matrix operations. The > performance advantage is shown in the attachment compared with OpenBlas. > Can we add KML_BLAS support to numpy? > > Cheers, > Chunlin Fang(github ID:Qiyu8) > > > Thanks, I hadn't heard of this library before. I am a bit confused as to > the link: did you mean this? > https://www.huaweicloud.com/kunpeng/software/KML_BLAS.html > > > Is there something beyond choosing KML_BLAS in the site.cfg file that > needs to be done to support it? > Support in numpy.distutils probably, analogous to what we did for BLIS for example: https://github.com/numpy/numpy/pull/7294/files. The other thing could be "ship aarch64 wheels with KML_BLAS support instead of OpenBLAS". That we can only do if KML_BLAS would be open source. Cheers, Ralf What is the license/redistribution policy? > Matti > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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