feature: knn kd tree fix for parallel

[Alexandr-Solovev]

Description

PR introduces memory optimizations in Knn KD-tree algorithm. Correct reindexing in parallel mode has been added. Queue helper has been updated.

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PR completeness and readability

• I have reviewed my changes thoroughly before submitting this pull request.
• I have commented my code, particularly in hard-to-understand areas.
• I have updated the documentation to reflect the changes or created a separate PR with update and provided its number in the description, if necessary.
• Git commit message contains an appropriate signed-off-by string (see CONTRIBUTING.md for details).
• I have added a respective label(s) to PR if I have a permission for that.
• I have resolved any merge conflicts that might occur with the base branch.

Testing

• I have run it locally and tested the changes extensively.
• All CI jobs are green or I have provided justification why they aren't.
• I have extended testing suite if new functionality was introduced in this PR.

Performance

• I have measured performance for affected algorithms using scikit-learn_bench and provided at least summary table with measured data, if performance change is expected.
• I have provided justification why performance has changed or why changes are not expected.
• I have provided justification why quality metrics have changed or why changes are not expected.
• I have extended benchmarking suite and provided corresponding scikit-learn_bench PR if new measurable functionality was introduced in this PR.

[Alexandr-Solovev]

/intelci: run

[Alexandr-Solovev]

/intelci: run

[Alexandr-Solovev]

/intelci: run

[Alexandr-Solovev]

/intelci: run

[Vika-F]

Would be interesting to see how this change impacts the performance.
But anyway, the change is justified as it fixes the crashes.

[Alexandr-Solovev]

/intelci: run

[Alexsandruss]

Next example gives seg. fault with this PR:

from sklearnex.neighbors import KNeighborsClassifier
from sklearn.datasets import make_classification
import logging as lg


lg.getLogger().setLevel("DEBUG")

x, y = make_classification(
    n_samples=200000,
    n_features=8,
    random_state=42,
)
knn = KNeighborsClassifier(algorithm="kd_tree")
knn.fit(x, y)
print("Score:", knn.score(x, y))

Backtrace:

daal::algorithms::kdtree_knn_classification::prediction::internal::KNNClassificationPredictKernel<double, (daal::algorithms::kdtree_knn_classification::prediction::Method)0, (daal::CpuType)6>::findNearestNeighbors(double const*, daal::internal::Heap<daal::internal::GlobalNeighbors<double, (daal::CpuType)6>, (daal::CpuType)6>&, daal::algorithms::kdtree_knn_classification::internal::Stack<daal::algorithms::kdtree_knn_classification::prediction::internal::SearchNode, (daal::CpuType)6>&, unsigned long, double, daal::algorithms::kdtree_knn_classification::interface1::KDTreeTable const&, unsigned long, daal::data_management::interface1::NumericTable const&, bool, daal::services::internal::DynamicArray<double*, daal::services::internal::ScalableMalloc<double*, (daal::CpuType)6>, daal::services::internal::DefaultConstructionPolicy<double*, (daal::CpuType)6, true>, (daal::CpuType)6>&)