Improved GpuArrowEvalPythonExec #783
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Signed-off-by: Robert (Bobby) Evans <bobby@apache.org>
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revans2
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Sep 16, 2020
| " when calling cuDF APIs in the UDF, also accelerates the data transfer between the" + | ||
| " Java process and Python process", | ||
| "The backend of the Scalar Pandas UDFs. Accelerates the data transfer between the" + | ||
| " Java process and Python process. It also supports running the Python UDFs code on" + |
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This probably needs some work and a reference to how to enable/configure the cudf on GPU feature, but I am nto sure what to say because we are not 100% ready to promote that yet.
jlowe
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Sep 17, 2020
sql-plugin/src/main/scala/com/nvidia/spark/rapids/SpillableColumnarBatch.scala
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* Improved GpuArrowEvalPythonExec Signed-off-by: Robert (Bobby) Evans <bobby@apache.org>
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* Improved GpuArrowEvalPythonExec Signed-off-by: Robert (Bobby) Evans <bobby@apache.org>
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…IDIA#783) Signed-off-by: spark-rapids automation <70000568+nvauto@users.noreply.github.com> Signed-off-by: spark-rapids automation <70000568+nvauto@users.noreply.github.com>
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This gets GpuArrowEvalPythonExec to the point that I think it can be on by default. From my testing with this the performance is either better than the original version (for fast python UDFs) or is as good (for slow UDFs simulated using a sleep) but with much lower CPU utilization so it is likely that that it would end up being faster because there would be less CPU contention.
I did fix one bug in the existing code to make this work. In
SpillableColumnarBatchif we try to create the batch from an batch ofGpuColumnVectorFromBufferI messed up the reference counting and things were being deleted before they should be, which ended up resulting in NullPointerExceptions instead of the double free or other similar error messages that are simpler to debug.