GPU support for DynamicPruningExpression and InSubqueryExec #9091
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Signed-off-by: Jason Lowe <jlowe@nvidia.com>
Signed-off-by: Jason Lowe <jlowe@nvidia.com>
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Manually tested that this PR resolves the fallback issues in the NDS benchmrak for DynamicPruningExpression and InSubqueryExec on AWS EMR 6.12. |
revans2
previously approved these changes
Aug 22, 2023
sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuDynamicPruningExpression.scala
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tests/src/test/spark330/scala/org/apache/spark/sql/rapids/GpuInSubqueryExecSuite.scala
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jlowe
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revans2
previously approved these changes
Aug 23, 2023
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revans2
previously approved these changes
Aug 23, 2023
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Seeing some odd results when benchmarking this at scale, marking this as draft while it is investigated. |
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The runs at scale are quite noisy. This seems to significantly help NDS query 37 and 82, but the results for the remaining queries that had DynamicPruningExpression and InSubqueryExec fallbacks on AWS EMR 6.12 were too noisy to tell. |
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revans2
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Aug 28, 2023
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This adds GPU support for DynamicPruningExpression and InSubqueryExec which has been seen outside of the normal DPP filter expressions within a scan on some Spark platforms. The implementations are relatively straightforward with the caveat that GpuInSubqueryExec needs to ensure the results are serialized to the executor since we do not participate in codegen like the CPU version does which the CPU relies on to get the results to the executor (i.e.: results are in the generated source code sent to the executors).
The support is only added for Spark 3.3+ on non-Databricks platforms. Databricks has a different number of arguments for InSubqueryExec that would need to be investigated, and Spark versions prior to 3.3 also have differing arguments. We don't see InSubqueryExec on any Spark platforms before Spark 3.3+, so not having a GPU replacement on these platforms should not be an issue in practice.