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https://issues.apache.org/jira/browse/SPARK-59535?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot updated SPARK-59535:
-----------------------------------
Labels: pull-request-available (was: )
> Run PCA covariance decomposition on an executor
> -----------------------------------------------
>
> Key: SPARK-59535
> URL: https://issues.apache.org/jira/browse/SPARK-59535
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 5.0.0
> Reporter: Ruifeng Zheng
> Priority: Major
> Labels: pull-request-available
>
> For PCA with at most 65535 features, RowMatrix currently aggregates a packed
> covariance matrix
> across executors, returns that large aggregate to the driver, expands it into
> a dense matrix, and
> runs the local Breeze SVD on the driver. The driver must hold the packed
> matrix, dense covariance,
> and SVD workspace even though only the smaller principal-component result
> needs to be returned.
> Add an internal tree-aggregation variant that preserves the final aggregate
> as a single-partition
> RDD. Use it in RowMatrix so covariance expansion and PCA decomposition run in
> the final executor
> task, and return only the public method result to the driver. Public APIs and
> numerical behavior
> remain unchanged.
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