FEA Add support for float32 on PairwiseDistancesReduction using Tempita
#23865
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Thanks for the updated PR. I assume that merging with main is needed before starting to review this. Could you run the benchmarks with more imbalanced train / tests, e.g. I wonder if the performance slowdown for a very large number of threads is caused by the fact that we have two few chunks to execute per-thread and using more imbalanced benchmark cases might validate (or invalidate) this hypothesis. |
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still, a 50x speed-up w.r.t. |
You're welcome! I did not know if this PR was submitted correctly while travelling (🍀), another one is to come for a new
Yes, I am quite glad we can reach those performance. I don't think we need to adapt the chunk size for the float32 case because there's little additional memory due to data-structures (the extra datastructures memory-wise are just the original
Yes. Let's try that.
I share the same hypothesis. I think we can explore a strategies to have a minimal number of batch per thread in another PR (a task I have added in the TODO list in the description). |
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I've adapted the description with updated benchmarks script and results. It looks like the implementations scales well on the The drop is mainly present when using too much threads. I think the Probably we could have had advertised the practical support for really large datasets more in the changelog for 1.1? :) |
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WOW |
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
It looks like yes. IMO, even if it's suboptimal, restrictive and hard to maintain on the long run, it's rather a pragmatic solution given where we are at today. From IRL discussions this week 🍀, it looks like @adrinjalali is interested to experiment with alternatives, like Rust. I think it's worth exploring, but might add complexity especially on the build setup and on interfacing with other libraries like BLAS and colleagues. Probably the work on #22438 might help? |
sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pxd.tp
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sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pyx.tp
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Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
sklearn/metrics/_pairwise_distances_reduction/_gemm_term_computer.pxd.tp
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Done with: grep -rl need_upcast . | xargs sed -i's/need_upcast/upcast_to_float64/g' Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
jjerphan
changed the title
PairwiseDistancesReduction to 32bit using TempitaPairwiseDistancesReduction using Tempita
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
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Thanks @jjerphan 🎉 |
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Thanks @ogrisel and @thomasjpfan for the reviews! |
This update the branch after the merge of scikit-learn#23865.
…pita (scikit-learn#23865) Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
…pita (scikit-learn#23865) Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
Reference Issues/PRs
Follows-up #22134
What does this implement/fix? Explain your changes.
This ports
PairwiseDistancesReductionand other implementations to 32bit using Tempita.Benchmarks results
The hardware scalability plateaus at 64 threads because, asymptotically and using Adham's law, 2.5% of the code (which parts of it are due to interaction with CPython) is sequential.
Improved hardware scalability beyond that point, mean removing the last portions of sequential code accounting for the few points of percentage left.
Raw results
Details
Benchmarks results between
main(a5d50cf) and this PR @ 31b8b28 (via 2c842bd)Between ×1.2 and, well, ×250+ speed-ups: it looks like it just scales linearly.
Regressions are due to using too many cores when the size of the problem (i.e.
n_trainandn_test) is small.1 thread
2 threads
4 threads
8 threads
16 threads
32 threads
64 threads
128 threads
Benchmarks information
Machine specification