diff --git a/integration_tests/src/main/python/hashing_test.py b/integration_tests/src/main/python/hashing_test.py
index e3ef67bc9f9..5180418c8e0 100644
--- a/integration_tests/src/main/python/hashing_test.py
+++ b/integration_tests/src/main/python/hashing_test.py
@@ -36,7 +36,7 @@
 
 _struct_of_xxhash_gens = StructGen([(f"c{i}", g) for i, g in enumerate(_atomic_gens)])
 
-# will be used by HyperLogLogPlusPLus(approx_count_distinct)
+# This is also used by HyperLogLogPlusPLus(approx_count_distinct)
 xxhash_gens = (_atomic_gens + [_struct_of_xxhash_gens] + single_level_array_gens
                + nested_array_gens_sample + [
                    all_basic_struct_gen,
diff --git a/integration_tests/src/main/python/hyper_log_log_plus_plus_test.py b/integration_tests/src/main/python/hyper_log_log_plus_plus_test.py
new file mode 100644
index 00000000000..3b9ebfe927e
--- /dev/null
+++ b/integration_tests/src/main/python/hyper_log_log_plus_plus_test.py
@@ -0,0 +1,38 @@
+# Copyright (c) 2021-2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import pytest
+
+from asserts import assert_gpu_and_cpu_are_equal_sql
+from data_gen import *
+from hashing_test import xxhash_gens
+from marks import ignore_order
+
+
+@ignore_order(local=True)
+@pytest.mark.parametrize('data_gen', xxhash_gens, ids=idfn)
+def test_hllpp_groupby(data_gen):
+    assert_gpu_and_cpu_are_equal_sql(
+        lambda spark: gen_df(spark, [("c1", int_gen), ("c2", data_gen)]),
+        "tab",
+        "select c1, APPROX_COUNT_DISTINCT(c2) from tab group by c1")
+
+
+@ignore_order(local=True)
+@pytest.mark.parametrize('data_gen', xxhash_gens, ids=idfn)
+def test_hllpp_reduction(data_gen):
+    assert_gpu_and_cpu_are_equal_sql(
+        lambda spark: unary_op_df(spark, data_gen),
+        "tab",
+        "select APPROX_COUNT_DISTINCT(a) from tab")
diff --git a/sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuOverrides.scala b/sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuOverrides.scala
index c1ede7554a6..9a1b1dfc2bb 100644
--- a/sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuOverrides.scala
+++ b/sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuOverrides.scala
@@ -3988,6 +3988,36 @@ object GpuOverrides extends Logging {
           GpuDynamicPruningExpression(child)
         }
       }),
+    expr[HyperLogLogPlusPlus](
+      "Aggregation approximate count distinct",
+      ExprChecks.reductionAndGroupByAgg(TypeSig.LONG, TypeSig.LONG,
+        // HyperLogLogPlusPlus depends on Xxhash64
+        // HyperLogLogPlusPlus supports all the types that Xxhash 64 supports
+        Seq(ParamCheck("input",XxHash64Shims.supportedTypes, TypeSig.all))),
+      (a, conf, p, r) => new UnaryExprMeta[HyperLogLogPlusPlus](a, conf, p, r) {
+
+        // It's the same as Xxhash64
+        override def tagExprForGpu(): Unit = {
+          val maxDepth = a.children.map(
+            c => XxHash64Utils.computeMaxStackSize(c.dataType)).max
+          if (maxDepth > Hash.MAX_STACK_DEPTH) {
+            willNotWorkOnGpu(s"The data type requires a stack depth of $maxDepth, " +
+                s"which exceeds the GPU limit of ${Hash.MAX_STACK_DEPTH}. " +
+                "The algorithm to calculate stack depth: " +
+                "1: Primitive type counts 1 depth; " +
+                "2: Array of Structure counts:  1  + depthOf(Structure); " +
+                "3: Array of Other counts: depthOf(Other); " +
+                "4: Structure counts: 1 + max of depthOf(child); " +
+                "5: Map counts: 2 + max(depthOf(key), depthOf(value)); "
+            )
+          }
+        }
+
+        override def convertToGpu(child: Expression): GpuExpression = {
+          GpuHyperLogLogPlusPlus(child, a.relativeSD)
+        }
+      }
+    ),
     SparkShimImpl.ansiCastRule
   ).collect { case r if r != null => (r.getClassFor.asSubclass(classOf[Expression]), r)}.toMap
 
diff --git a/sql-plugin/src/main/scala/org/apache/spark/sql/rapids/aggregate/GpuHyperLogLogPlusPlus.scala b/sql-plugin/src/main/scala/org/apache/spark/sql/rapids/aggregate/GpuHyperLogLogPlusPlus.scala
new file mode 100644
index 00000000000..f78ef47a5e4
--- /dev/null
+++ b/sql-plugin/src/main/scala/org/apache/spark/sql/rapids/aggregate/GpuHyperLogLogPlusPlus.scala
@@ -0,0 +1,181 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.rapids.aggregate
+
+import scala.collection.immutable.Seq
+
+import ai.rapids.cudf
+import ai.rapids.cudf.{DType, GroupByAggregation, ReductionAggregation}
+import com.nvidia.spark.rapids._
+import com.nvidia.spark.rapids.Arm.withResourceIfAllowed
+import com.nvidia.spark.rapids.RapidsPluginImplicits.ReallyAGpuExpression
+import com.nvidia.spark.rapids.jni.HyperLogLogPlusPlusHostUDF
+import com.nvidia.spark.rapids.shims.ShimExpression
+
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Expression}
+import org.apache.spark.sql.rapids.{GpuCreateNamedStruct, GpuGetStructField}
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.vectorized.ColumnarBatch
+
+case class CudfHLLPP(override val dataType: DataType,
+    precision: Int) extends CudfAggregate {
+  override lazy val reductionAggregate: cudf.ColumnVector => cudf.Scalar =
+    (input: cudf.ColumnVector) => input.reduce(
+      ReductionAggregation.hostUDF(
+        HyperLogLogPlusPlusHostUDF.createHLLPPHostUDF(
+          HyperLogLogPlusPlusHostUDF.AggregationType.Reduction, precision)),
+      DType.STRUCT)
+  override lazy val groupByAggregate: GroupByAggregation =
+    GroupByAggregation.hostUDF(
+      HyperLogLogPlusPlusHostUDF.createHLLPPHostUDF(
+        HyperLogLogPlusPlusHostUDF.AggregationType.GroupBy, precision)
+    )
+  override val name: String = "CudfHyperLogLogPlusPlus"
+}
+
+case class CudfMergeHLLPP(override val dataType: DataType,
+    precision: Int)
+    extends CudfAggregate {
+  override lazy val reductionAggregate: cudf.ColumnVector => cudf.Scalar =
+    (input: cudf.ColumnVector) => input.reduce(
+      ReductionAggregation.hostUDF(
+        HyperLogLogPlusPlusHostUDF.createHLLPPHostUDF(
+          HyperLogLogPlusPlusHostUDF.AggregationType.Reduction_MERGE, precision)),
+      DType.STRUCT)
+  override lazy val groupByAggregate: GroupByAggregation =
+    GroupByAggregation.hostUDF(
+      HyperLogLogPlusPlusHostUDF.createHLLPPHostUDF(
+        HyperLogLogPlusPlusHostUDF.AggregationType.GroupByMerge, precision)
+    )
+  override val name: String = "CudfMergeHyperLogLogPlusPlus"
+}
+
+/**
+ * Perform the final evaluation step to compute approximate count distinct from sketches.
+ * Input is long columns, first construct struct of long then feed to cuDF
+ */
+case class GpuHyperLogLogPlusPlusEvaluation(childExpr: Expression,
+    precision: Int)
+    extends GpuExpression with ShimExpression {
+  override def dataType: DataType = LongType
+
+  override def nullable: Boolean = false
+
+  override def prettyName: String = "HyperLogLogPlusPlus_evaluation"
+
+  override def children: scala.Seq[Expression] = Seq(childExpr)
+
+  override def columnarEval(batch: ColumnarBatch): GpuColumnVector = {
+    withResourceIfAllowed(childExpr.columnarEval(batch)) { sketches =>
+      val distinctValues = HyperLogLogPlusPlusHostUDF.estimateDistinctValueFromSketches(
+        sketches.getBase, precision)
+      GpuColumnVector.from(distinctValues, LongType)
+    }
+  }
+}
+
+/**
+ * Gpu version of HyperLogLogPlusPlus
+ * Spark APPROX_COUNT_DISTINCT on NULLs returns zero
+ */
+case class GpuHyperLogLogPlusPlus(childExpr: Expression, relativeSD: Double)
+    extends GpuAggregateFunction with Serializable {
+
+  // Consistent with Spark
+  private lazy val precision: Int =
+    Math.ceil(2.0d * Math.log(1.106d / relativeSD) / Math.log(2.0d)).toInt;
+
+  private lazy val numRegistersPerSketch: Int = 1 << precision;
+
+  // Each long contains 10 register(max 6 bits)
+  private lazy val numWords = numRegistersPerSketch / 10 + 1
+
+  // Spark agg buffer type: long array
+  private lazy val sparkAggBufferAttributes: Seq[AttributeReference] = {
+    Seq.tabulate(numWords) { i =>
+      AttributeReference(s"MS[$i]", LongType)()
+    }
+  }
+
+  /**
+   * Spark uses long columns to save agg buffer, e.g.: long[52]
+   * Each long compacts multiple registers to save memory
+   */
+  override val aggBufferAttributes: Seq[AttributeReference] = sparkAggBufferAttributes
+
+  /**
+   * init long array with all zero
+   */
+  override lazy val initialValues: Seq[Expression] = Seq.tabulate(numWords) { _ =>
+    GpuLiteral(0L, LongType)
+  }
+
+  override lazy val inputProjection: Seq[Expression] = Seq(childExpr)
+
+  /**
+   * cuDF HLLPP sketch type: struct<long, ..., long>
+   */
+  private lazy val cuDFBufferType: DataType = StructType.fromAttributes(aggBufferAttributes)
+
+  /**
+   * cuDF uses Struct<long, ..., long> column to do aggregate
+   */
+  override lazy val updateAggregates: Seq[CudfAggregate] =
+    Seq(CudfHLLPP(cuDFBufferType, precision))
+
+  /**
+   * Convert long columns to Struct<long, ..., long> column
+   */
+  private def genStruct: Seq[Expression] = {
+    val names = Seq.tabulate(numWords) { i => GpuLiteral(s"MS[$i]", StringType) }
+    Seq(GpuCreateNamedStruct(names.zip(aggBufferAttributes).flatten { case (a, b) => List(a, b) }))
+  }
+
+  /**
+   * Convert Struct<long, ..., long> column to long columns
+   */
+  override lazy val postUpdate: Seq[Expression] = Seq.tabulate(numWords) {
+    i => GpuGetStructField(postUpdateAttr.head, i)
+  }
+
+  /**
+   * convert to Struct<long, ..., long>
+   */
+  override lazy val preMerge: Seq[Expression] = genStruct
+
+  override lazy val mergeAggregates: Seq[CudfAggregate] =
+    Seq(CudfMergeHLLPP(cuDFBufferType, precision))
+
+  /**
+   * Convert Struct<long, ..., long> column to long columns
+   */
+  override lazy val postMerge: Seq[Expression] = Seq.tabulate(numWords) {
+    i => GpuGetStructField(postMergeAttr.head, i)
+  }
+
+  override lazy val evaluateExpression: Expression =
+    GpuHyperLogLogPlusPlusEvaluation(genStruct.head, precision)
+
+  override def dataType: DataType = LongType
+
+  // Spark APPROX_COUNT_DISTINCT on NULLs returns zero
+  override def nullable: Boolean = false
+
+  override def prettyName: String = "approx_count_distinct"
+
+  override def children: Seq[Expression] = Seq(childExpr)
+}
diff --git a/tools/generated_files/330/operatorsScore.csv b/tools/generated_files/330/operatorsScore.csv
index e86e30e606c..6707e7a0c00 100644
--- a/tools/generated_files/330/operatorsScore.csv
+++ b/tools/generated_files/330/operatorsScore.csv
@@ -139,6 +139,7 @@ HiveGenericUDF,4
 HiveHash,4
 HiveSimpleUDF,4
 Hour,4
+HyperLogLogPlusPlus,4
 Hypot,4
 If,4
 In,4
diff --git a/tools/generated_files/330/supportedExprs.csv b/tools/generated_files/330/supportedExprs.csv
index aa744638220..9d0268b7f33 100644
--- a/tools/generated_files/330/supportedExprs.csv
+++ b/tools/generated_files/330/supportedExprs.csv
@@ -713,6 +713,10 @@ First,S,`first_value`; `first`,None,reduction,input,S,S,S,S,S,S,S,S,PS,S,S,S,S,N
 First,S,`first_value`; `first`,None,reduction,result,S,S,S,S,S,S,S,S,PS,S,S,S,S,NS,PS,PS,PS,NS,NS,NS
 First,S,`first_value`; `first`,None,window,input,S,S,S,S,S,S,S,S,PS,S,S,S,S,NS,PS,PS,PS,NS,NS,NS
 First,S,`first_value`; `first`,None,window,result,S,S,S,S,S,S,S,S,PS,S,S,S,S,NS,PS,PS,PS,NS,NS,NS
+HyperLogLogPlusPlus,S,`approx_count_distinct`,None,aggregation,input,S,S,S,S,S,S,S,S,PS,S,S,NS,S,NS,NS,NS,NS,NS,NS,NS
+HyperLogLogPlusPlus,S,`approx_count_distinct`,None,aggregation,result,NA,NA,NA,NA,S,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA
+HyperLogLogPlusPlus,S,`approx_count_distinct`,None,reduction,input,S,S,S,S,S,S,S,S,PS,S,S,NS,S,NS,NS,NS,NS,NS,NS,NS
+HyperLogLogPlusPlus,S,`approx_count_distinct`,None,reduction,result,NA,NA,NA,NA,S,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA
 Last,S,`last_value`; `last`,None,aggregation,input,S,S,S,S,S,S,S,S,PS,S,S,S,S,NS,PS,PS,PS,NS,NS,NS
 Last,S,`last_value`; `last`,None,aggregation,result,S,S,S,S,S,S,S,S,PS,S,S,S,S,NS,PS,PS,PS,NS,NS,NS
 Last,S,`last_value`; `last`,None,reduction,input,S,S,S,S,S,S,S,S,PS,S,S,S,S,NS,PS,PS,PS,NS,NS,NS