[Numpy-discussion] Re: ndarray.sort x86 dispatch
On Wed, 2023-01-04 at 04:06 +, Peter Schneider-Kamp wrote: > Hi guys, > > I am trying to understand how the x86 dispatch for ndarray sort > works. The following call in Line 137 of > numpy/core/src/npysort/quicksort.cpp returns 0 for my test cases: > > if (x86_dispatch::quicksort(start, num)) > return 0; > > I have tried to compile with --cpu-dispatch="AVX512_KNL AVX512_CLX > AVX512_CNL AVX512_ICL AVX512_SKX" but for dtype=uint64 (or int64 or > uint8 or float32 or float64) it always the same result, i.e., the > standard quicksort is used instead of the AVX512 one with bitonic > sorting base cases. > > What do I have to do to be able to use the AVX512 implementation? You can check what is found with: np.show_runtime() Also just google your CPU or check `cat /proc/cpuinfo`. AVX512 exists currently only on high end Intel CPUs (IIRC), presumably, you simply do not have the hardware. E.g. on M1, `show_runtime()` won't even list AVX512 when compiled, on x86 they would probably show up as "not found" because they exist, but your hardware doesn't support AVX512. - Sebastian > > I am currently compiling on a MacBook Pro with Monterey. I have all > kinds of Linux machines available, if that should be a requirements. > > Thanks in advance for any insights! > > Cheers, > Peter > -- > Peter Schneider-Kamp > Professor in Artificial Intelligence > Department of Mathematics & Computer Science > University of Southern Denmark > > ___ > NumPy-Discussion mailing list -- numpy-discussion@python.org > To unsubscribe send an email to numpy-discussion-le...@python.org > https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ > Member address: sebast...@sipsolutions.net ___ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
[Numpy-discussion] Re: ndarray.sort x86 dispatch
Hi Sebastian,
This is what I get when I compile with just “python3.8 setup.py build -j 16”:
- BEGIN -
### CLIB COMPILER OPTIMIZATION ###
INFO: Platform :
Architecture: x64
Compiler: gcc
CPU baseline :
Requested : 'min'
Enabled : SSE SSE2 SSE3
Flags : -msse -msse2 -msse3
Extra checks: none
CPU dispatch :
Requested : 'max -xop -fma4'
Enabled : SSSE3 SSE41 POPCNT SSE42 AVX F16C FMA3 AVX2 AVX512F AVX512CD
AVX512_KNL AVX512_KNM AVX512_SKX AVX512_CLX AVX512_CNL AVX512_ICL
Generated : none
- END -
I was wondering why it says “Generated : none” for the CPU dispatch?
This is the output of “np.show_runtime()”:
- BEGIN -
[{'numpy_version': '0+untagged.31149.g6d474f2',
'python': '3.8.10 (default, Nov 14 2022, 12:59:47) \n[GCC 9.4.0]',
'uname': uname_result(system='Linux', node='lambda',
release='5.4.0-135-generic', version='#152-Ubuntu SMP Wed Nov 23 20:19:22 UTC
2022', machine='x86_64', processor='x86_64')},
{'simd_extensions': {'baseline': ['SSE', 'SSE2', 'SSE3'],
'found': ['SSSE3',
'SSE41',
'POPCNT',
'SSE42',
'AVX',
'F16C',
'FMA3',
'AVX2',
'AVX512F',
'AVX512CD',
'AVX512_SKX'],
'not_found': ['AVX512_KNL',
'AVX512_KNM',
'AVX512_CLX',
'AVX512_CNL',
'AVX512_ICL']}}]
- END -
Apparently, the CPU supports AVX512_SKX, i.e., avx512bw, avx512dq, avx512vl
(bold face is mine, rest is from /cpu/procinfo):
- BEGIN -
model name : Intel(R) Core(TM) i9-9820X CPU @ 3.30GHz
flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca
cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx
pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl
xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx
est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe
popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch
cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti ssbd mba ibrs ibpb stibp
tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2
smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap
clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1
xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln
pts hwp hwp_act_window hwp_epp hwp_pkg_req md_clear flush_l1d arch_capabilities
- END -
Am I building numpy incorrectly?
I really need to be able to execute the AVX512 quicksort implementation as part
of a research project, where we generate efficient sorting implementations that
we would like to contribute to numpy to the degree that they improve upon
existing solutions.
Any help is highly appreciated!
Cheers,
Peter
From: Sebastian Berg
Date: Friday, 6 January 2023 at 08.04
To: numpy-discussion@python.org
Subject: [Numpy-discussion] Re: ndarray.sort x86 dispatch
[You don't often get email from sebast...@sipsolutions.net. Learn why this is
important at https://aka.ms/LearnAboutSenderIdentification ]
On Wed, 2023-01-04 at 04:06 +, Peter Schneider-Kamp wrote:
> Hi guys,
>
> I am trying to understand how the x86 dispatch for ndarray sort
> works. The following call in Line 137 of
> numpy/core/src/npysort/quicksort.cpp returns 0 for my test cases:
>
> if (x86_dispatch::quicksort(start, num))
> return 0;
>
> I have tried to compile with --cpu-dispatch="AVX512_KNL AVX512_CLX
> AVX512_CNL AVX512_ICL AVX512_SKX" but for dtype=uint64 (or int64 or
> uint8 or float32 or float64) it always the same result, i.e., the
> standard quicksort is used instead of the AVX512 one with bitonic
> sorting base cases.
>
> What do I have to do to be able to use the AVX512 implementation?
You can check what is found with:
np.show_runtime()
Also just google your CPU or check `cat /proc/cpuinfo`. AVX512 exists
currently only on high end Intel CPUs (IIRC), presumably, you simply do
not have the hardware.
E.g. on M1, `show_runtime()` won't even list AVX512 when compiled, on
x86 they would probably show up as "not found" because they exist, but
your hardware doesn't support AVX512.
- Sebastian
>
> I am currently compiling on a MacBook Pro with Monterey. I have all
> kinds of Linux machines available, if that should be a requirements.
>
> Thanks in advance for any insights!
>
> Cheers,
> Peter
> --
> Peter Schneider-Kamp
> Professor in Artificial Intelligenc
[Numpy-discussion] Documentation Team update
Hi folks, I am happy to announce an update to the NumPy Documentation team. Although there is no formal leadership position so far, it is helpful to have someone responsible for running team meetings, organizing high-level items and planning larger projects such as Google Season of Docs. With this in mind, I am happy to report that going forward, Ross Barnowski and Mukulika Pahari will be leading the Docs team. Ross is a NumPy maintainer and has been involved with the Docs team for a while, including co-mentoring Google Season of Docs projects and setting up NumPy Tutorials. Mukulika joined the NumPy documentation team as a technical writer during the 2021 Google Season of Docs program. Since then, she has contributed new content (How-To guides and Tutorials) and restructured several parts of the documentation for better readability. She is also a maintainer of https://github.com/numpy/numpy-tutorials. Thank you, Mukulika and Ross, for stepping up! P.S. Inessa Pawson, our Contributor Experience Lead, has put together a NumPy Docs Team handbook [1] which is meant to serve as guidance for the new leadership team and those who will be doing this work in the future. [1] https://github.com/numpy/archive/tree/main/numpy_docs_team_handbook ___ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
[Numpy-discussion] Re: Documentation Team update
Congratulations, Mukulika and Ross! We appreciate all your hard work and dedication to the project. On Thu, Jan 5, 2023 at 8:01 PM Melissa Mendonça wrote: > Hi folks, > > I am happy to announce an update to the NumPy Documentation team. Although > there is no formal leadership position so far, it is helpful to have > someone responsible for running team meetings, organizing high-level items > and planning larger projects such as Google Season of Docs. With this in > mind, I am happy to report that going forward, Ross Barnowski and Mukulika > Pahari will be leading the Docs team. > > Ross is a NumPy maintainer and has been involved with the Docs team for a > while, including co-mentoring Google Season of Docs projects and setting up > NumPy Tutorials. Mukulika joined the NumPy documentation team as a > technical writer during the 2021 Google Season of Docs program. Since then, > she has contributed new content (How-To guides and Tutorials) and > restructured several parts of the documentation for better readability. She > is also a maintainer of https://github.com/numpy/numpy-tutorials. > > Thank you, Mukulika and Ross, for stepping up! > > P.S. Inessa Pawson, our Contributor Experience Lead, has put together a > NumPy Docs Team handbook [1] which is meant to serve as guidance for the > new leadership team and those who will be doing this work in the future. > > [1] https://github.com/numpy/archive/tree/main/numpy_docs_team_handbook > ___ > NumPy-Discussion mailing list -- numpy-discussion@python.org > To unsubscribe send an email to numpy-discussion-le...@python.org > https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ > Member address: albusc...@gmail.com > -- Cheers, Inessa Inessa Pawson Contributor Experience Lead | NumPy https://numpy.org/ GitHub: inessapawson ___ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
