Add GPU ArrayAggregate for SUM/PRODUCT/MAX/MIN/ALL/ANY

docs/additional-functionality/advanced_configs.md

@@ -197,6 +197,7 @@ Name | SQL Function(s) | Description | Default Value | Notes
 <a name="sql.expression.Alias"></a>spark.rapids.sql.expression.Alias| |Gives a column a name|true|None|
 <a name="sql.expression.And"></a>spark.rapids.sql.expression.And|`and`|Logical AND|true|None|
 <a name="sql.expression.AnsiCast"></a>spark.rapids.sql.expression.AnsiCast| |Convert a column of one type of data into another type|true|None|
+<a name="sql.expression.ArrayAggregate"></a>spark.rapids.sql.expression.ArrayAggregate|`aggregate`|Aggregate elements in an array using an accumulator function and finishing transformation. Currently only lambdas of the form (acc, x) -> acc + g(x) with an identity finish are executed on the GPU; other shapes fall back to CPU.|true|None|
 <a name="sql.expression.ArrayContains"></a>spark.rapids.sql.expression.ArrayContains|`array_contains`|Returns a boolean if the array contains the passed in key|true|None|
 <a name="sql.expression.ArrayDistinct"></a>spark.rapids.sql.expression.ArrayDistinct|`array_distinct`|Removes duplicate values from the array|true|None|
 <a name="sql.expression.ArrayExcept"></a>spark.rapids.sql.expression.ArrayExcept|`array_except`|Returns an array of the elements in array1 but not in array2, without duplicates|true|This is not 100% compatible with the Spark version because the GPU implementation treats -0.0 and 0.0 as equal, but the CPU implementation currently does not (see SPARK-39845). Also, Apache Spark 3.1.3 fixed issue SPARK-36741 where NaNs in these set like operators were not treated as being equal. We have chosen to break with compatibility for the older versions of Spark in this instance and handle NaNs the same as 3.1.3+|