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@cyb70289 cyb70289 commented Oct 26, 2022

As xsimd only supports fixed-size SVE, we have to specify vector size
explicitly on command line. And the binary can only run on hardware
with matched vector size. Otherwise, the code behaviour is undefined.
E.g., cmake -DARROW_SIMD_LEVEL=SVE256 ..
According macro ARROW_HAVE_SVE256 and cmake variable are defined.

We can also leverage compiler auto vectorization to generate size
agnostic SVE code without specifying the vector size.
E.g., cmake -DARROW_SIMD_LEVEL=SVE ..

This PR also removes some unused Arm64 arch options.

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⚠️ Ticket has not been started in JIRA, please click 'Start Progress'.

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pitrou commented Oct 26, 2022

It seems we could also use SVE in src/arrow/util/bpacking.cc, src/arrow/util/utf8_internal.h, as well as src/arrow/csv/lexing_internal.h and src/arrow/csv/writer.cc?

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It seems we could also use SVE in src/arrow/util/bpacking.cc, src/arrow/util/utf8_internal.h, as well as src/arrow/csv/lexing_internal.h and src/arrow/csv/writer.cc?

Yes. SVE may be better when longer vector size is beneficial, or to leverage operations (e.g, scatter/gather) not availble in NEON.

As xsimd only supports fixed-size SVE, we have to specify vector size
explicitly on command line. And the binary can only run on hardware
with that vector size. Otherwise, the code behaviour is undefined.
E.g., `cmake -DARROW_SIMD_LEVEL=SVE256 ..`
According macro `ARROW_HAVE_SVE256` and cmake variable are defined.

We can also leverage compiler auto vectorization to generate size
agnostic SVE code without specifying the vector size.
E.g., `cmake -DARROW_SIMD_LEVEL=SVE ..`

This PR also removes some unused Arm64 arch options.
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@github-actions crossbow submit conda-{linux,osx}-*

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Unable to match any tasks for `conda-{linux,osx}-*`
The Archery job run can be found at: https://github.com/apache/arrow/actions/runs/3360036059

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@github-actions crossbow submit conda-linux-*

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Revision: f99b57a

Submitted crossbow builds: ursacomputing/crossbow @ actions-19dd1864f2

Task Status
conda-linux-gcc-py310-arm64 Azure
conda-linux-gcc-py310-cpu Azure
conda-linux-gcc-py310-cuda Azure
conda-linux-gcc-py310-ppc64le Azure
conda-linux-gcc-py37-arm64 Azure
conda-linux-gcc-py37-cpu-r40 Azure
conda-linux-gcc-py37-cpu-r41 Azure
conda-linux-gcc-py37-cuda Azure
conda-linux-gcc-py37-ppc64le Azure
conda-linux-gcc-py38-arm64 Azure
conda-linux-gcc-py38-cpu Azure
conda-linux-gcc-py38-cuda Azure
conda-linux-gcc-py38-ppc64le Azure
conda-linux-gcc-py39-arm64 Azure
conda-linux-gcc-py39-cpu Azure
conda-linux-gcc-py39-cuda Azure
conda-linux-gcc-py39-ppc64le Azure

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@github-actions crossbow submit conda-osx-*

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Revision: f99b57a

Submitted crossbow builds: ursacomputing/crossbow @ actions-457a7b106d

Task Status
conda-osx-arm64-clang-py310 Azure
conda-osx-arm64-clang-py37 Azure
conda-osx-arm64-clang-py38 Azure
conda-osx-arm64-clang-py39 Azure
conda-osx-clang-py310 Azure
conda-osx-clang-py37 Azure
conda-osx-clang-py37-r40 Azure
conda-osx-clang-py37-r41 Azure
conda-osx-clang-py38 Azure
conda-osx-clang-py39 Azure

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cyb70289 commented Nov 1, 2022

@cyb70289 cyb70289 requested review from kou and pitrou November 1, 2022 01:45
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+1 for changes

I re-ran failed jobs.

@cyb70289 cyb70289 merged commit 16fd7f3 into apache:master Nov 1, 2022
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ursabot commented Nov 1, 2022

Benchmark runs are scheduled for baseline = 2a5c773 and contender = 16fd7f3. 16fd7f3 is a master commit associated with this PR. Results will be available as each benchmark for each run completes.
Conbench compare runs links:
[Finished ⬇️0.0% ⬆️0.0%] ec2-t3-xlarge-us-east-2
[Failed ⬇️0.0% ⬆️0.0%] test-mac-arm
[Finished ⬇️0.54% ⬆️0.0%] ursa-i9-9960x
[Finished ⬇️0.32% ⬆️0.18%] ursa-thinkcentre-m75q
Buildkite builds:
[Finished] 16fd7f3f ec2-t3-xlarge-us-east-2
[Failed] 16fd7f3f test-mac-arm
[Finished] 16fd7f3f ursa-i9-9960x
[Finished] 16fd7f3f ursa-thinkcentre-m75q
[Finished] 2a5c7736 ec2-t3-xlarge-us-east-2
[Failed] 2a5c7736 test-mac-arm
[Finished] 2a5c7736 ursa-i9-9960x
[Finished] 2a5c7736 ursa-thinkcentre-m75q
Supported benchmarks:
ec2-t3-xlarge-us-east-2: Supported benchmark langs: Python, R. Runs only benchmarks with cloud = True
test-mac-arm: Supported benchmark langs: C++, Python, R
ursa-i9-9960x: Supported benchmark langs: Python, R, JavaScript
ursa-thinkcentre-m75q: Supported benchmark langs: C++, Java

@cyb70289 cyb70289 deleted the sve branch November 1, 2022 13:39
kou pushed a commit that referenced this pull request Nov 15, 2022
As xsimd only supports fixed-size SVE, we have to specify vector size
explicitly on command line. And the binary can only run on hardware
with matched vector size. Otherwise, the code behaviour is undefined.
E.g., `cmake -DARROW_SIMD_LEVEL=SVE256 ..`
According macro `ARROW_HAVE_SVE256` and cmake variable are defined.

We can also leverage compiler auto vectorization to generate size
agnostic SVE code without specifying the vector size.
E.g., `cmake -DARROW_SIMD_LEVEL=SVE ..`

This PR also removes some unused Arm64 arch options.

Authored-by: Yibo Cai <[email protected]>
Signed-off-by: Yibo Cai <[email protected]>
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4 participants