Re: [Numpy-discussion] Call for Reviewers

[ChunLin Fang]

Hi all,

I've reviewed and submitted several Numpy PRs before, Glad to have the
right to add tags.my github id is Qiyu8 <https://github.com/Qiyu8>.

Thanks,
ChunLin Fang
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[Numpy-discussion] Armv8 server donation

[ChunLin Fang]

Hi, all:
I noticed that the shippable CI always skipped after PR submitted , The
reason why it's skip seems to be "No active nodes found in shared node pool
"shippable_shared_aarch64""
Potential bugs may buried through out numpy without shippable CI.
I happened to own an idle armv8 server that can donate to numpy
community, please let me know if that can improve numpy's CI/CD environment
, also need somebody help me set up the CI/CD environment on that server.

Best wishes

Fang ChunLin.
https://github.com/Qiyu8
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[Numpy-discussion] ENH: Proposal to add KML_BLAS support

[ChunLin Fang]

  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)
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[Numpy-discussion] ENH: Proposal to add KML_BLAS support

[ChunLin Fang]

Part of the performance benchmark results.


KML_BLAS VS OpenBLAS.xlsx
Description: MS-Excel 2007 spreadsheet
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Re: [Numpy-discussion] ENH: Proposal to add KML_BLAS support

[ChunLin Fang]

Thanks for asking, this is a simple explanation for your questions:
1. The download link of KML_BLAS:
The Chinese page is
https://www.huaweicloud.com/kunpeng/software/KML_BLAS.html
The English page is https://kunpeng.huawei.com/en/#/developer/devkit/library,
you can find a "Math Library" Navigation entry in the bottom of this page.
"KML_BLAS" lies in there.
2. The license/redistribution policy of KML_BLAS:
The license is very similar to intel MKL, The license file is in the
process of making.
3.How to support KML_BLAS:
The support process is similar to BLIS, just need to add to
numpy.distutils, KML_BLAS will not open source in the near future.
4.What kind of ARM chips are supported:
any ARMV8 chip is supported.
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