diff --git a/benchmarks/CMakeLists.txt b/benchmarks/CMakeLists.txt
index c66e373e7..019830b55 100644
--- a/benchmarks/CMakeLists.txt
+++ b/benchmarks/CMakeLists.txt
@@ -101,3 +101,7 @@ ConfigureNVBench(RETRIEVE_BENCH
 # - reduce_by_key benchmarks ----------------------------------------------------------------------
 set(RBK_BENCH_SRC "${CMAKE_CURRENT_SOURCE_DIR}/reduce_by_key/reduce_by_key.cu")
 ConfigureBench(RBK_BENCH "${RBK_BENCH_SRC}")
+
+###################################################################################################
+ConfigureNVBench(BLOOM_FILTER_BENCH
+  "bloom_filter/bloom_filter_bench.cu")
diff --git a/benchmarks/analysis/notebooks/bloom_filter_bench.ipynb b/benchmarks/analysis/notebooks/bloom_filter_bench.ipynb
new file mode 100644
index 000000000..6d2880aa6
--- /dev/null
+++ b/benchmarks/analysis/notebooks/bloom_filter_bench.ipynb
@@ -0,0 +1,261 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Preparation"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "source": [
+    "!pip3 install pandas\n",
+    "!pip3 install matplotlib\n",
+    "\n",
+    "# Import libraries\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "from collections import namedtuple\n",
+    "\n",
+    "#plt.style.use('seaborn-white')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: pandas in /home/djuenger/miniconda3/lib/python3.9/site-packages (1.3.1)\n",
+      "Requirement already satisfied: numpy>=1.17.3 in /home/djuenger/miniconda3/lib/python3.9/site-packages (from pandas) (1.21.1)\n",
+      "Requirement already satisfied: pytz>=2017.3 in /home/djuenger/miniconda3/lib/python3.9/site-packages (from pandas) (2021.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /home/djuenger/miniconda3/lib/python3.9/site-packages (from pandas) (2.8.2)\n",
+      "Requirement already satisfied: six>=1.5 in /home/djuenger/miniconda3/lib/python3.9/site-packages (from python-dateutil>=2.7.3->pandas) (1.16.0)\n",
+      "Requirement already satisfied: matplotlib in /home/djuenger/miniconda3/lib/python3.9/site-packages (3.4.2)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in /home/djuenger/miniconda3/lib/python3.9/site-packages (from matplotlib) (8.3.1)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/djuenger/miniconda3/lib/python3.9/site-packages (from matplotlib) (1.3.1)\n",
+      "Requirement already satisfied: pyparsing>=2.2.1 in /home/djuenger/miniconda3/lib/python3.9/site-packages (from matplotlib) (2.4.7)\n",
+      "Requirement already satisfied: cycler>=0.10 in /home/djuenger/miniconda3/lib/python3.9/site-packages (from matplotlib) (0.10.0)\n",
+      "Requirement already satisfied: numpy>=1.16 in /home/djuenger/miniconda3/lib/python3.9/site-packages (from matplotlib) (1.21.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /home/djuenger/miniconda3/lib/python3.9/site-packages (from matplotlib) (2.8.2)\n",
+      "Requirement already satisfied: six in /home/djuenger/miniconda3/lib/python3.9/site-packages (from cycler>=0.10->matplotlib) (1.16.0)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "# helper functions\n",
+    "\n",
+    "style_ = namedtuple(\"style_\", [\"color\", \"marker\", \"linestyle\"])\n",
+    "\n",
+    "def load_csv_files(csv_files):\n",
+    "    dfs = {}\n",
+    "    for key, fname in csv_files.items():\n",
+    "        df = pd.read_csv(fname)\n",
+    "        dfs[key] = df[df[\"Skipped\"] == \"No\"]\n",
+    "    return dfs\n",
+    "\n",
+    "def filter_bench(dfs, query):\n",
+    "    if isinstance(dfs, dict):\n",
+    "        filtered_dfs = {}\n",
+    "        for key in dfs.keys():\n",
+    "            filtered_dfs[key] = dfs[key].query(query)\n",
+    "        return filtered_dfs\n",
+    "    else:\n",
+    "        return dfs.query(query)\n",
+    "\n",
+    "def plot_bench(dfs, xlabel, styles, show_legend=True, title=None, ofname=None, show_xlabel=True, show_ylabel=True, log_xscale=False, log_yscale=False, font_size=14):\n",
+    "    fig, ax = plt.subplots(1, 1)\n",
+    "\n",
+    "    ax.tick_params(labelsize=font_size)\n",
+    "    if(show_ylabel):\n",
+    "        ax.set_xlabel(xlabel, fontsize=font_size)\n",
+    "    if(show_ylabel):\n",
+    "        ax.set_ylabel(\"Operations per second\", fontsize=font_size)\n",
+    "    if(log_xscale):\n",
+    "        ax.set_xscale('log')\n",
+    "    if(log_yscale):\n",
+    "        ax.set_yscale('log')\n",
+    "    ax.set_title(title, fontsize=font_size)\n",
+    "    ax.grid()\n",
+    "\n",
+    "    for key, df in dfs.items():        \n",
+    "        style = styles[key]\n",
+    "\n",
+    "        Y = df[\"NumInputs\"].unique()[0]/df[\"GPU Time (sec)\"]\n",
+    "\n",
+    "        if xlabel in df.columns:\n",
+    "            X = df[xlabel]\n",
+    "    \n",
+    "            ax.plot(X, Y, label=key, color=style.color, marker=style.marker, linestyle=style.linestyle)\n",
+    "            ax.scatter(X, Y, color=style.color, marker=style.marker, linestyle=style.linestyle)\n",
+    "        else:\n",
+    "            ax.axhline(y=Y.iloc[0], label=key, color=style.color, linestyle=style.linestyle)\n",
+    "\n",
+    "    if(show_legend):\n",
+    "        plt.legend(fontsize=font_size - 4)\n",
+    "\n",
+    "    if(ofname):\n",
+    "        plt.savefig(ofname, dpi=1200, format='png', bbox_inches='tight')\n",
+    "\n",
+    "    plt.show()"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "source": [
+    "# GMEM\n",
+    "dfs = load_csv_files({\"V100\" : \"../results/bloom_filter_v100.csv\", \n",
+    "                      \"A100\" : \"../results/bloom_filter_a100.csv\"})\n",
+    "                          \n",
+    "dfs[\"V100\"][\"Filter Size [MB]\"] = dfs[\"V100\"][\"NumBits\"] / 8 / 1000 / 1000\n",
+    "dfs[\"A100\"][\"Filter Size [MB]\"] = dfs[\"A100\"][\"NumBits\"] / 8 / 1000 / 1000\n",
+    "\n",
+    "dfs[\"V100 INSERT\"] = dfs[\"V100\"].query('Operation == \"INSERT\"')\n",
+    "dfs[\"V100 CONTAINS\"] = dfs[\"V100\"].query('Operation == \"CONTAINS\"')\n",
+    "dfs[\"A100 INSERT\"] = dfs[\"A100\"].query('Operation == \"INSERT\"')\n",
+    "dfs[\"A100 CONTAINS\"] = dfs[\"A100\"].query('Operation == \"CONTAINS\"')\n",
+    "del dfs[\"V100\"]\n",
+    "del dfs[\"A100\"]\n",
+    "\n",
+    "styles = {\n",
+    "    \"V100 INSERT\" : style_('b', 'x', '-'),\n",
+    "    \"V100 CONTAINS\" : style_('b', 'x', '--'),\n",
+    "    \"A100 INSERT\" : style_('r', 'o', '-'),\n",
+    "    \"A100 CONTAINS\" : style_('r', 'o', '--')}\n",
+    "\n",
+    "query = 'Skipped   == \"No\" and\\\n",
+    "         KeyType   == \"I32\" and\\\n",
+    "         SlotType  == \"I32\" and\\\n",
+    "         NumHashes == 2 and\\\n",
+    "         NumInputs == 1000000000'\n",
+    "\n",
+    "print(\"INSERT/CONTAINS on V100/A100 (GMEM)\")\n",
+    "plot_bench(filter_bench(dfs, query), \"Filter Size [MB]\", styles=styles, log_xscale=True)\n",
+    "\n",
+    "query = query + ' and NumBits > 100000000'\n",
+    "plot_bench(filter_bench(dfs, query), \"Filter Size [MB]\", styles=styles)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "INSERT/CONTAINS on V100/A100 (GMEM)\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ],
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "source": [
+    "# GMEM vs. L2\n",
+    "df = pd.read_csv(\"../results/bloom_filter_scope_hit_ratio_60.csv\")\n",
+    "df = df[df[\"Skipped\"] == \"No\"]\n",
+    "df[\"Filter Size [MB]\"] = df[\"NumBits\"] / 8 / 1000 / 1000\n",
+    "\n",
+    "dfs = {}\n",
+    "dfs[\"GMEM INSERT\"] = df.query('Scope == \"GMEM\" and Operation == \"INSERT\"')\n",
+    "dfs[\"GMEM CONTAINS\"] = df.query('Scope == \"GMEM\" and Operation == \"CONTAINS\"')\n",
+    "dfs[\"L2 INSERT\"] = df.query('Scope == \"L2\" and Operation == \"INSERT\"')\n",
+    "dfs[\"L2 CONTAINS\"] = df.query('Scope == \"L2\" and Operation == \"CONTAINS\"')\n",
+    "\n",
+    "styles = {\n",
+    "    \"GMEM INSERT\" : style_('b', 'x', '-'),\n",
+    "    \"GMEM CONTAINS\" : style_('b', 'x', '--'),\n",
+    "    \"L2 INSERT\" : style_('r', 'o', '-'),\n",
+    "    \"L2 CONTAINS\" : style_('r', 'o', '--')}\n",
+    "    \n",
+    "query = 'KeyType   == \"I32\" and\\\n",
+    "         SlotType  == \"I32\" and\\\n",
+    "         NumHashes == 2 and\\\n",
+    "         NumInputs == 50000000'\n",
+    "\n",
+    "print(\"INSERT on A100 (GMEM vs. L2)\")\n",
+    "plot_bench(filter_bench(dfs, query), \"Filter Size [MB]\", styles=styles)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "INSERT on A100 (GMEM vs. L2)\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ],
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"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "fab55a90acef312968e5bff70ae91c3267a5b896b51d076af77c4418fdb5d582"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.9.5 64-bit ('base': conda)"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.5",
+   "mimetype": "text/x-python",
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "pygments_lexer": "ipython3",
+   "nbconvert_exporter": "python",
+   "file_extension": ".py"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/benchmarks/bloom_filter/bloom_filter_bench.cu b/benchmarks/bloom_filter/bloom_filter_bench.cu
new file mode 100644
index 000000000..e8da3691e
--- /dev/null
+++ b/benchmarks/bloom_filter/bloom_filter_bench.cu
@@ -0,0 +1,307 @@
+/*
+ * Copyright (c) 2021-2022, 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.
+ */
+
+#include <cuco/bloom_filter.cuh>
+#include <cuco/detail/cache_residency_control.cuh>
+
+#include <nvbench/nvbench.cuh>
+
+#include <cuda/std/atomic>
+
+#include <thrust/count.h>
+#include <thrust/device_vector.h>
+#include <thrust/sequence.h>
+
+#include <cstddef>
+
+#include <cooperative_groups.h>
+
+namespace cg = cooperative_groups;
+
+static constexpr nvbench::int64_t block_size = 256;
+static constexpr nvbench::int64_t stride     = 4;
+
+enum class FilterOperation { INSERT, CONTAINS };
+
+NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
+  FilterOperation,
+  [](FilterOperation op) {
+    switch (op) {
+      case FilterOperation::INSERT: return "INSERT";
+      case FilterOperation::CONTAINS: return "CONTAINS";
+      default: return "ERROR";
+    }
+  },
+  [](FilterOperation op) {
+    switch (op) {
+      case FilterOperation::INSERT: return "FilterOperation::INSERT";
+      case FilterOperation::CONTAINS: return "FilterOperation::CONTAINS";
+      default: return "ERROR";
+    }
+  })
+
+enum class FilterScope { GMEM, L2 };
+
+NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
+  FilterScope,
+  [](FilterScope s) {
+    switch (s) {
+      case FilterScope::GMEM: return "GMEM";
+      case FilterScope::L2: return "L2";
+      default: return "ERROR";
+    }
+  },
+  [](FilterScope s) {
+    switch (s) {
+      case FilterScope::GMEM: return "FilterScope::GMEM";
+      case FilterScope::L2: return "FilterScope::L2";
+      default: return "ERROR";
+    }
+  })
+
+enum class DataScope { GMEM, REGS };
+
+NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
+  DataScope,
+  [](DataScope s) {
+    switch (s) {
+      case DataScope::GMEM: return "GMEM";
+      case DataScope::REGS: return "REGS";
+      default: return "ERROR";
+    }
+  },
+  [](DataScope s) {
+    switch (s) {
+      case DataScope::GMEM: return "DataScope::GMEM";
+      case DataScope::REGS: return "DataScope::REGS";
+      default: return "ERROR";
+    }
+  })
+
+template <typename Key, typename Slot>
+void add_size_summary(nvbench::state& state)
+{
+  using filter_type =
+    cuco::bloom_filter<Key, cuda::thread_scope_device, cuco::cuda_allocator<char>, Slot>;
+
+  auto const num_keys   = state.get_int64("NumInputs");
+  auto const num_bits   = state.get_int64("NumBits");
+  auto const num_hashes = state.get_int64("NumHashes");
+
+  filter_type filter(num_bits, num_hashes);
+
+  auto& summ = state.add_summary("nv/filter/size/mb");
+  summ.set_string("hint", "FilterMB");
+  summ.set_string("short_name", "FilterMB");
+  summ.set_string("description", "Size of the Bloom filter in MB.");
+  summ.set_float64("value", filter.get_num_slots() * sizeof(Slot) / 1000 / 1000);
+}
+
+template <typename Key, typename Slot>
+void add_fpr_summary(nvbench::state& state)
+{
+  using filter_type =
+    cuco::bloom_filter<Key, cuda::thread_scope_device, cuco::cuda_allocator<char>, Slot>;
+
+  auto const num_keys   = state.get_int64("NumInputs");
+  auto const num_bits   = state.get_int64("NumBits");
+  auto const num_hashes = state.get_int64("NumHashes");
+
+  thrust::device_vector<Key> keys(num_keys * 2);
+  thrust::sequence(thrust::device, keys.begin(), keys.end(), 1);
+  thrust::device_vector<bool> result(num_keys, false);
+
+  auto tp_begin = keys.begin();
+  auto tp_end   = tp_begin + num_keys;
+  auto tn_begin = tp_end;
+  auto tn_end   = keys.end();
+
+  filter_type filter(num_bits, num_hashes);
+  filter.insert(tp_begin, tp_end);
+  filter.contains(tn_begin, tn_end, result.begin());
+
+  float fp = thrust::count(thrust::device, result.begin(), result.end(), true);
+
+  auto& summ = state.add_summary("nv/filter/fpr");
+  summ.set_string("hint", "FPR");
+  summ.set_string("short_name", "FPR");
+  summ.set_string("description", "False-positive rate of the bloom filter.");
+  summ.set_float64("value", fp / num_keys);
+}
+
+template <nvbench::int64_t BLOCK_SIZE, typename Filter, typename InputIt>
+__global__ void __launch_bounds__(BLOCK_SIZE)
+  insert_kernel(Filter mutable_view, InputIt first, InputIt last)
+{
+  std::size_t tid = block_size * blockIdx.x + threadIdx.x;
+  auto it         = first + tid;
+
+  while (it < last) {
+    mutable_view.insert(*it);
+    it += gridDim.x * BLOCK_SIZE;
+  }
+}
+
+template <nvbench::int64_t BLOCK_SIZE, typename Filter, typename InputIt, typename OutputIt>
+__global__ void __launch_bounds__(BLOCK_SIZE)
+  contains_kernel(Filter view, InputIt first, InputIt last, OutputIt results)
+{
+  std::size_t tid = block_size * blockIdx.x + threadIdx.x;
+
+  while ((first + tid) < last) {
+    *(results + tid) = view.contains(*(first + tid));
+    tid += gridDim.x * BLOCK_SIZE;
+  }
+}
+
+template <nvbench::int64_t BLOCK_SIZE, typename Filter>
+__global__ void __launch_bounds__(BLOCK_SIZE)
+  insert_kernel(Filter mutable_view, nvbench::int64_t num_keys)
+{
+  using key_type = typename Filter::key_type;
+
+  auto g = cg::this_grid();
+
+  for (key_type key = g.thread_rank(); key < num_keys; key += g.size()) {
+    mutable_view.insert(key);
+  }
+}
+
+template <nvbench::int64_t BLOCK_SIZE, typename Filter>
+__global__ void __launch_bounds__(BLOCK_SIZE)
+  contains_kernel(Filter view, nvbench::int64_t num_keys)
+{
+  using key_type = typename Filter::key_type;
+
+  auto g = cg::this_grid();
+
+  for (key_type key = g.thread_rank(); key < num_keys; key += g.size()) {
+    volatile bool contains = view.contains(key);
+  }
+}
+
+template <typename Key, typename Slot, FilterOperation Op, FilterScope FScope, DataScope DScope>
+void nvbench_cuco_bloom_filter(nvbench::state& state,
+                               nvbench::type_list<Key,
+                                                  Slot,
+                                                  nvbench::enum_type<Op>,
+                                                  nvbench::enum_type<FScope>,
+                                                  nvbench::enum_type<DScope>>)
+{
+  auto num_keys   = state.get_int64("NumInputs");
+  auto num_bits   = state.get_int64("NumBits");
+  auto num_hashes = state.get_int64("NumHashes");
+
+  [[maybe_unused]] thrust::device_vector<Key> keys;
+  [[maybe_unused]] thrust::device_vector<bool> results;
+
+  if constexpr (DScope == DataScope::GMEM) {
+    keys.resize(num_keys);
+    thrust::sequence(thrust::device, keys.begin(), keys.end(), 1);
+
+    if constexpr (Op == FilterOperation::CONTAINS) { results.resize(num_keys); }
+  }
+
+  using filter_type =
+    cuco::bloom_filter<Key, cuda::thread_scope_device, cuco::cuda_allocator<char>, Slot>;
+
+  filter_type filter(num_bits, num_hashes);
+  auto mutable_view           = filter.get_device_mutable_view();
+  auto view                   = filter.get_device_view();
+  std::size_t const grid_size = SDIV(num_keys, stride * block_size);
+
+  state.add_element_count(num_keys);
+  state.add_global_memory_writes<Slot>(num_keys);
+
+  add_fpr_summary<Key, Slot>(state);
+  add_size_summary<Key, Slot>(state);
+
+  if constexpr (Op == FilterOperation::CONTAINS) {
+    insert_kernel<block_size><<<grid_size, block_size>>>(mutable_view, num_keys);
+  }
+
+  cudaStream_t stream;
+  cudaStreamCreate(&stream);
+
+  if constexpr (FScope == FilterScope::L2)
+    cuco::register_l2_persistence(
+      stream, filter.get_slots(), filter.get_slots() + filter.get_num_slots());
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(stream));
+
+  state.exec([&](nvbench::launch& launch) {
+    if constexpr (Op == FilterOperation::INSERT) {
+      filter.initialize(launch.get_stream());
+      if constexpr (DScope == DataScope::GMEM) {
+        insert_kernel<block_size><<<grid_size, block_size, 0, launch.get_stream()>>>(
+          mutable_view, keys.begin(), keys.end());
+      }
+      if constexpr (DScope == DataScope::REGS) {
+        insert_kernel<block_size>
+          <<<grid_size, block_size, 0, launch.get_stream()>>>(mutable_view, num_keys);
+      }
+    }
+    if constexpr (Op == FilterOperation::CONTAINS) {
+      if constexpr (DScope == DataScope::GMEM) {
+        contains_kernel<block_size><<<grid_size, block_size, 0, launch.get_stream()>>>(
+          view, keys.begin(), keys.end(), results.begin());
+      }
+      if constexpr (DScope == DataScope::REGS) {
+        contains_kernel<block_size>
+          <<<grid_size, block_size, 0, launch.get_stream()>>>(view, num_keys);
+      }
+    }
+  });
+
+  if constexpr (FScope == FilterScope::L2) cuco::unregister_l2_persistence(stream);
+}
+
+using key_type_range  = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
+using slot_type_range = nvbench::type_list<nvbench::int32_t, nvbench::uint64_t>;
+using op_range        = nvbench::enum_type_list<FilterOperation::INSERT, FilterOperation::CONTAINS>;
+using filter_scope_range = nvbench::enum_type_list<FilterScope::GMEM, FilterScope::L2>;
+using data_scope_range   = nvbench::enum_type_list<DataScope::GMEM, DataScope::REGS>;
+
+// A100 L2 = 40MB ~ 330'000'000 bits
+// smem = 48kb ~ 390'0000 bits
+// 1GB ~ 8'500'000'000 bits
+// 4GB ~ 34'000'000'000 bits
+
+NVBENCH_BENCH_TYPES(nvbench_cuco_bloom_filter,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::type_list<nvbench::int64_t>,
+                                      op_range,
+                                      filter_scope_range,
+                                      data_scope_range))
+  .set_name("cuco_bloom_filter_l2")
+  .set_type_axes_names({"KeyType", "SlotType", "FilterOperation", "FilterScope", "DataScope"})
+  .set_max_noise(3)
+  .add_int64_axis("NumInputs", {10'000'000, 100'000'000})
+  .add_int64_axis("NumBits", {300'000'000})
+  .add_int64_axis("NumHashes", {2});
+
+NVBENCH_BENCH_TYPES(nvbench_cuco_bloom_filter,
+                    NVBENCH_TYPE_AXES(key_type_range,
+                                      slot_type_range,
+                                      op_range,
+                                      nvbench::enum_type_list<FilterScope::GMEM>,
+                                      data_scope_range))
+  .set_name("cuco_bloom_filter_gmem")
+  .set_type_axes_names({"KeyType", "SlotType", "FilterOperation", "FilterScope", "DataScope"})
+  .set_max_noise(3)
+  .add_int64_axis("NumInputs", {1'000'000'000, 100'000'000})
+  .add_int64_axis("NumBits", {8'500'000'000, 34'000'000'000})
+  .add_int64_axis("NumHashes", {6});
\ No newline at end of file
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index db40f3cf2..521990b57 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -37,6 +37,8 @@ ConfigureExample(STATIC_MAP_HOST_BULK_EXAMPLE "${CMAKE_CURRENT_SOURCE_DIR}/stati
 ConfigureExample(STATIC_MAP_DEVICE_SIDE_EXAMPLE "${CMAKE_CURRENT_SOURCE_DIR}/static_map/device_view_example.cu")
 ConfigureExample(STATIC_MAP_CUSTOM_TYPE_EXAMPLE "${CMAKE_CURRENT_SOURCE_DIR}/static_map/custom_type_example.cu")
 ConfigureExample(STATIC_MULTIMAP_HOST_BULK_EXAMPLE "${CMAKE_CURRENT_SOURCE_DIR}/static_multimap/host_bulk_example.cu")
+ConfigureExample(BLOOM_FILTER_HOST_BULK_EXAMPLE "${CMAKE_CURRENT_SOURCE_DIR}/bloom_filter/host_bulk_example.cu")
+ConfigureExample(BLOOM_FILTER_L2_RESIDENCY_EXAMPLE "${CMAKE_CURRENT_SOURCE_DIR}/bloom_filter/l2_residency_example.cu")
 
 foreach(arch IN LISTS CMAKE_CUDA_ARCHITECTURES)
     if("${arch}" MATCHES "^6")
diff --git a/examples/bloom_filter/host_bulk_example.cu b/examples/bloom_filter/host_bulk_example.cu
new file mode 100644
index 000000000..8bec29ce6
--- /dev/null
+++ b/examples/bloom_filter/host_bulk_example.cu
@@ -0,0 +1,68 @@
+/*
+ * Copyright (c) 2021, 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.
+ */
+
+#include <cuco/bloom_filter.cuh>
+
+#include <thrust/count.h>
+#include <thrust/device_vector.h>
+#include <thrust/execution_policy.h>
+#include <thrust/sequence.h>
+
+#include <iostream>
+
+int main(void)
+{
+  // Generate 10'000 keys and insert the first 5'000 into the filter.
+  int const num_keys = 10'000;
+  int const num_tp   = num_keys * 0.5;
+  int const num_tn   = num_keys - num_tp;
+
+  // Spawn a filter with 1'000'000 bits and 6-bit patterns for each key.
+  cuco::bloom_filter<int> filter{num_tp * 10, 6};
+
+  thrust::device_vector<int> keys(num_keys);
+  thrust::sequence(keys.begin(), keys.end(), 1);
+
+  auto tp_begin = keys.begin();
+  auto tp_end   = tp_begin + num_tp;
+  auto tn_begin = tp_end;
+  auto tn_end   = keys.end();
+
+  // Insert the first half of the keys.
+  filter.insert(tp_begin, tp_end);
+
+  thrust::device_vector<bool> tp_result(num_tp, false);
+  thrust::device_vector<bool> tn_result(num_keys - num_tp, false);
+
+  // Query the filter for the previously inserted keys.
+  // This should result in a true-positive rate of TPR=1.
+  filter.contains(tp_begin, tp_end, tp_result.begin());
+
+  // Query the filter for the keys that are not present in the filter.
+  // Since bloom filters are probalistic data structures, the filter
+  // exhibits a false-positive rate FPR>0 depending on the number of bits in
+  // the filter and the number of hashes used per key.
+  filter.contains(tn_begin, tn_end, tn_result.begin());
+
+  float tp_rate =
+    float(thrust::count(thrust::device, tp_result.begin(), tp_result.end(), true)) / float(num_tp);
+  float fp_rate =
+    float(thrust::count(thrust::device, tn_result.begin(), tn_result.end(), true)) / float(num_tn);
+
+  std::cout << "TPR=" << tp_rate << " FPR=" << fp_rate << std::endl;
+
+  return 0;
+}
diff --git a/examples/bloom_filter/l2_residency_example.cu b/examples/bloom_filter/l2_residency_example.cu
new file mode 100644
index 000000000..528f061c1
--- /dev/null
+++ b/examples/bloom_filter/l2_residency_example.cu
@@ -0,0 +1,83 @@
+/*
+ * Copyright (c) 2022, 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.
+ */
+
+#include <cuco/bloom_filter.cuh>
+#include <cuco/detail/cache_residency_control.cuh>
+
+#include <thrust/device_vector.h>
+
+#include <iostream>
+
+int main(void)
+{
+  int const num_keys   = 10'000'000;
+  int const num_bits   = 300'000'000;  // 37 MB; fits in the L2 of an A100
+  int const num_hashes = 2;            // sufficient for small filters
+
+  // Spawn a 37MB filter and 2-bit patterns for each key.
+  cuco::bloom_filter<int> filter{num_bits, num_hashes};
+
+  // Create a CUDA stream in which this operation is performed.
+  cudaStream_t stream;
+  cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking);
+
+  thrust::device_vector<int> keys(num_keys);
+  thrust::sequence(keys.begin(), keys.end(), 1);
+  thrust::device_vector<bool> contains(num_keys);
+
+  // Insert all keys and subsequently query them against the filter; measure runtime
+  cudaEvent_t gmem_start, gmem_stop;
+  cudaEventCreate(&gmem_start);
+  cudaEventCreate(&gmem_stop);
+
+  cudaEventRecord(gmem_start, stream);
+  filter.insert(keys.begin(), keys.end(), stream);
+  filter.contains(keys.begin(), keys.end(), contains.begin(), stream);
+  cudaEventRecord(gmem_stop, stream);
+  cudaStreamSynchronize(stream);
+
+  float gmem_delta;
+  cudaEventElapsedTime(&gmem_delta, gmem_start, gmem_stop);
+  std::cout << "Insert+query filter in global memory: " << gmem_delta << "ms\n";
+
+  // Re-initialize the filter, i.e., set all bits to zero
+  filter.initialize(stream);
+  cudaStreamSynchronize(stream);
+
+  // Make the filter persistent in the GPU's L2 cache
+  cuco::register_l2_persistence(
+    stream, filter.get_slots(), filter.get_slots() + filter.get_num_slots());
+
+  // Insert all keys and subsequently query them against the filter; measure runtime
+  cudaEvent_t l2_start, l2_stop;
+  cudaEventCreate(&l2_start);
+  cudaEventCreate(&l2_stop);
+
+  cudaEventRecord(l2_start, stream);
+  filter.insert(keys.begin(), keys.end(), stream);
+  filter.contains(keys.begin(), keys.end(), contains.begin(), stream);
+  cudaEventRecord(l2_stop, stream);
+  cudaStreamSynchronize(stream);
+
+  float l2_delta;
+  cudaEventElapsedTime(&l2_delta, l2_start, l2_stop);
+  std::cout << "Insert+query filter in L2: " << l2_delta << "ms\n";
+
+  // Flush the L2 so it can be used for other tasks
+  cuco::unregister_l2_persistence(stream);
+
+  return 0;
+}
diff --git a/include/cuco/bloom_filter.cuh b/include/cuco/bloom_filter.cuh
new file mode 100644
index 000000000..da576292b
--- /dev/null
+++ b/include/cuco/bloom_filter.cuh
@@ -0,0 +1,636 @@
+/*
+ * Copyright (c) 2021-2022, 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.
+ */
+
+#pragma once
+
+#include <cuco/allocator.hpp>
+#include <cuco/detail/__config>
+#include <cuco/detail/bloom_filter_kernels.cuh>
+#include <cuco/detail/error.hpp>
+#include <cuco/detail/hash_functions.cuh>
+#include <cuco/detail/utils.hpp>
+
+#include <cooperative_groups.h>
+
+#include <cuda/std/atomic>
+
+#if defined(CUCO_HAS_CUDA_BARRIER)
+#include <cuda/barrier>
+#endif
+
+#if defined(CUCO_HAS_CUDA_ANNOTATED_PTR)
+#include <cuda/annotated_ptr>
+#endif
+
+#include <cstddef>
+#include <memory>
+
+namespace cuco {
+
+/**
+ * @brief A GPU-accelerated, filter for approximate set membership queries.
+ *
+ * Allows constant time concurrent inserts or concurrent find operations from threads in device
+ * code.
+ *
+ * Current limitations:
+ * - Does not support erasing keys
+ * - Capacity is fixed and will not grow automatically
+ *
+ * The `bloom_filter` supports two types of operations:
+ * - Host-side "bulk" operations
+ * - Device-side "singular" operations
+ *
+ * The host-side bulk operations include `insert` and `contains`. These
+ * APIs should be used when there are a large number of keys to insert or lookup
+ * in the map. For example, given a range of keys specified by device-accessible
+ * iterators, the bulk `insert` function will insert all keys into the map.
+ *
+ * The singular device-side operations allow individual threads to perform
+ * independent insert or contains operations from device code. These
+ * operations are accessed through non-owning, trivially copyable "view" types:
+ * `device_view` and `mutable_device_view`. The `device_view` class is an
+ * immutable view that allows only non-modifying operations such as `contains`.
+ * The `mutable_device_view` class only allows `insert` operations.
+ * The two types are separate to prevent erroneous concurrent 'insert'/'contains'
+ * operations.
+ *
+ * Example:
+ * \code{.cpp}
+ * // TODO
+ * \endcode
+ *
+ *
+ * @tparam Key Arithmetic type used for key
+ * @tparam Scope The scope in which insert/find operations will be performed by
+ * individual threads.
+ * @tparam Allocator Type of allocator used for device storage
+ * @tparam Slot Type of bloom filter partition
+ */
+template <typename Key,
+          cuda::thread_scope Scope = cuda::thread_scope_device,
+          typename Allocator       = cuco::cuda_allocator<char>,
+          typename Slot            = std::uint64_t>
+class bloom_filter {
+ public:
+  using key_type            = Key;                             ///< Key type
+  using slot_type           = Slot;                            ///< Filter slot type
+  using atomic_slot_type    = cuda::atomic<slot_type, Scope>;  ///< Filter slot type
+  using iterator            = atomic_slot_type*;               ///< Filter slot iterator type
+  using const_iterator      = atomic_slot_type const*;         ///< Filter slot const iterator type
+  using allocator_type      = Allocator;                       ///< Allocator type
+  using slot_allocator_type = typename std::allocator_traits<Allocator>::rebind_alloc<
+    atomic_slot_type>;  ///< Type of the allocator to (de)allocate slots
+
+#if !defined(CUCO_HAS_INDEPENDENT_THREADS)
+  static_assert(atomic_slot_type::is_always_lock_free,
+                "A slot type larger than 8B is supported for only sm_70 and up.");
+#endif
+
+  bloom_filter(bloom_filter const&) = delete;
+  bloom_filter(bloom_filter&&)      = delete;
+  bloom_filter& operator=(bloom_filter const&) = delete;
+  bloom_filter& operator=(bloom_filter&&) = delete;
+
+  /**
+   * @brief Construct a fixed-size filter with the specified number of bits.
+   *
+   * @param num_bits The total number of bits in the filter
+   * @param num_hashes The number of hashes to be applied to a key
+   * @param alloc Allocator used for allocating device storage
+   * @param stream The CUDA stream this operation is executed in
+   */
+  bloom_filter(std::size_t num_bits,
+               std::size_t num_hashes,
+               Allocator const& alloc = Allocator{},
+               cudaStream_t stream    = 0);
+
+  /**
+   * @brief Destroys the filter and frees its contents.
+   *
+   */
+  ~bloom_filter();
+
+  /**
+   * @brief (Re-) initializes the filter, i.e., set all bits to 0.
+   *
+   * @param stream The CUDA stream this operation is executed in
+   */
+  void initialize(cudaStream_t stream = 0);
+
+  /**
+   * @brief Inserts all keys in the range `[first, last)`.
+   *
+   * @tparam InputIt Device accessible input iterator whose `value_type` is
+   * convertible to the filter's `key_type`
+   * @tparam Hash1 Unary callable type
+   * @tparam Hash2 Unary callable type
+   * @tparam Hash3 Unary callable type
+   * @tparam KeyEqual Binary callable type
+   * @param first Beginning of the sequence of keys
+   * @param last End of the sequence of keys
+   * @param stream The CUDA stream this operation is executed in
+   * @param hash1 First hash function; used to determine a filter slot
+   * @param hash2 Second hash function; used to generate a signature of the key
+   * @param hash3 Third hash function; used to generate a signature of the key
+   */
+  template <typename InputIt,
+            typename Hash1 = cuco::detail::MurmurHash3_32<key_type>,
+            typename Hash2 = Hash1,
+            typename Hash3 = Hash2>
+  void insert(InputIt first,
+              InputIt last,
+              cudaStream_t stream = 0,
+              Hash1 hash1         = Hash1{},
+              Hash2 hash2         = Hash2{1},
+              Hash3 hash3         = Hash3{2});
+
+  /**
+   * @brief Indicates whether the keys in the range `[first, last)` are
+   * contained in the filter.
+   *
+   * Writes a `bool` to `(output + i)` indicating if the signature of key
+   * `*(first + i)` is present in the filter.
+   *
+   * @tparam InputIt Device accessible input iterator whose `value_type` is
+   * convertible to the filter's `key_type`
+   * @tparam OutputIt Device accessible output iterator whose `value_type` is
+   * convertible to `bool`
+   * @tparam Hash1 Unary callable type
+   * @tparam Hash2 Unary callable type
+   * @tparam Hash3 Unary callable type
+   * @param first Beginning of the sequence of keys
+   * @param last End of the sequence of keys
+   * @param output_begin Beginning of the sequence of booleans for the presence of each key
+   * @param stream The CUDA stream this operation is executed in
+   * @param hash1 First hash function; used to determine a filter slot
+   * @param hash2 Second hash function; used to generate a signature of the key
+   * @param hash3 Third hash function; used to generate a signature of the key
+   */
+  template <typename InputIt,
+            typename OutputIt,
+            typename Hash1 = cuco::detail::MurmurHash3_32<key_type>,
+            typename Hash2 = Hash1,
+            typename Hash3 = Hash2>
+  void contains(InputIt first,
+                InputIt last,
+                OutputIt output_begin,
+                cudaStream_t stream = 0,
+                Hash1 hash1         = Hash1{},
+                Hash2 hash2         = Hash2{1},
+                Hash3 hash3         = Hash3{2});
+
+  /**
+   * @brief Gets slots array.
+   *
+   * @return Slots array
+   */
+  iterator get_slots() noexcept { return slots_; }
+
+  /**
+   * @brief Gets slots array.
+   *
+   * @return Slots array
+   */
+  const_iterator get_slots() const noexcept { return slots_; }
+
+  /**
+   * @brief Gets the total number of bits in the filter (rounded up to the
+   * next multiple of block size).
+   *
+   * @return The total number of bits in the filter.
+   */
+  std::size_t get_num_bits() const noexcept { return num_bits_; }
+
+  /**
+   * @brief Gets the total number of slots in the filter.
+   *
+   * @return The total number of slots in the filter.
+   */
+  std::size_t get_num_slots() const noexcept { return num_slots_; }
+
+  /**
+   * @brief Gets the number of hashes to apply to a key.
+   *
+   * @return The number of hashes to apply to a key.
+   */
+  std::size_t get_num_hashes() const noexcept { return num_hashes_; }
+
+ private:
+  class device_view_base {
+   protected:
+    // Import member type definitions from `bloom_filter`
+    using key_type         = Key;                      ///< Key type
+    using slot_type        = slot_type;                ///< Filter slot type
+    using atomic_slot_type = atomic_slot_type;         ///< Filter slot type
+    using iterator         = atomic_slot_type*;        ///< Filter slot iterator type
+    using const_iterator   = atomic_slot_type const*;  ///< Filter slot const iterator type
+
+   private:
+    atomic_slot_type* slots_{};  ///< Pointer to flat slots storage
+    std::size_t num_bits_{};     ///< Total number of bits
+    std::size_t num_slots_{};    ///< Total number of slots
+    std::size_t num_hashes_{};   ///< Number of hashes to apply
+
+   protected:
+    __host__ __device__ device_view_base(atomic_slot_type* slots,
+                                         std::size_t num_bits,
+                                         std::size_t num_hashes) noexcept
+      : slots_{slots},
+        num_bits_{SDIV(num_bits, detail::type_bits<slot_type>()) * detail::type_bits<slot_type>()},
+        num_slots_{SDIV(num_bits, detail::type_bits<slot_type>())},
+        num_hashes_{num_hashes}
+    {
+    }
+
+    /**
+     * @brief Returns the slot for a given key `k`
+     *
+     * @tparam Hash Unary callable type
+     * @param k The key to get the slot for
+     * @param hash The unary callable used to hash the key
+     * @return Pointer to the slot for `k`
+     */
+    template <typename Hash>
+    __device__ iterator key_slot(Key const& k, Hash hash) noexcept
+    {
+      return &slots_[hash(k) % num_slots_];
+    }
+
+    /**
+     * @brief Returns the slot for a given key `k`
+     *
+     * @tparam Hash Unary callable type
+     * @param k The key to get the slot for
+     * @param hash The unary callable used to hash the key
+     * @return Pointer to the slot for `k`
+     */
+    template <typename Hash>
+    __device__ const_iterator key_slot(Key const& k, Hash hash) const noexcept
+    {
+      return &slots_[hash(k) % num_slots_];
+    }
+
+    /**
+     * @brief Returns the bit pattern for a given key `k`
+     *
+     * @tparam Hash1 Unary callable type
+     * @tparam Hash2 Unary callable type
+     * @param k The key to calculate the pattern for
+     * @param hash1 First hash function; used to generate a signature of the key
+     * @param hash2 Second hash function; used to generate a signature of the key
+     * @return Bit pattern for key `k`
+     */
+    template <typename Hash1, typename Hash2>
+    __device__ slot_type key_pattern(Key const& k, Hash1 hash1, Hash2 hash2) const noexcept;
+
+    /**
+     * @brief Initializes the given array of slots using the threads in the group `g`.
+     *
+     * @note This function synchronizes the group `g`.
+     *
+     * @tparam CG The type of the cooperative thread group
+     * @param g The cooperative thread group used to initialize the slots
+     * @param slots Pointer to the array of slots to initialize
+     * @param num_slots Number of slots to initialize
+     */
+    template <typename CG>
+    __device__ static void initialize_slots(CG g, atomic_slot_type* slots, std::size_t num_bits)
+    {
+      auto num_slots = SDIV(num_bits, detail::type_bits<slot_type>());
+      auto tid       = g.thread_rank();
+      while (tid < num_slots) {
+        new (&slots[tid]) atomic_slot_type{0};
+        tid += g.size();
+      }
+      g.sync();
+    }
+
+   public:
+    /**
+     * @brief Gets slots array.
+     *
+     * @return Slots array
+     */
+    __host__ __device__ iterator get_slots() noexcept { return slots_; }
+
+    /**
+     * @brief Gets slots array.
+     *
+     * @return Slots array
+     */
+    __host__ __device__ const_iterator get_slots() const noexcept { return slots_; }
+
+    /**
+     * @brief Gets the total number of bits in the filter (rounded up to the
+     * next multiple of block size).
+     *
+     * @return The total number of bits in the filter.
+     */
+    __host__ __device__ std::size_t get_num_bits() const noexcept { return num_bits_; }
+
+    /**
+     * @brief Gets the total number of slots in the filter.
+     *
+     * @return The total number of slots in the filter.
+     */
+    __host__ __device__ std::size_t get_num_slots() const noexcept { return num_slots_; }
+
+    /**
+     * @brief Gets the number of hashes to apply to a key.
+     *
+     * @return The number of hashes to apply to a key.
+     */
+    __host__ __device__ std::size_t get_num_hashes() const noexcept { return num_hashes_; }
+
+    /**
+     * @brief Returns iterator to the first slot.
+     *
+     * @note Unlike `std::map::begin()`, the `begin_slot()` iterator does _not_ point to the first
+     * occupied slot. Instead, it refers to the first slot in the array of contiguous slot storage.
+     * Iterating from `begin_slot()` to `end_slot()` will iterate over all slots.
+     *
+     * There is no `begin()` iterator to avoid confusion.
+     *
+     * @return Iterator to the first slot
+     */
+    __device__ iterator begin_slot() noexcept { return slots_; }
+
+    /**
+     * @brief Returns iterator to the first slot.
+     *
+     * @note Unlike `std::map::begin()`, the `begin_slot()` iterator does _not_ point to the first
+     * occupied slot. Instead, it refers to the first slot in the array of contiguous slot storage.
+     * Iterating from `begin_slot()` to `end_slot()` will iterate over all slots.
+     *
+     * There is no `begin()` iterator to avoid confusion.
+     *
+     * @return Iterator to the first slot
+     */
+    __device__ const_iterator begin_slot() const noexcept { return slots_; }
+
+    /**
+     * @brief Returns a const_iterator to one past the last slot.
+     *
+     * @return A const_iterator to one past the last slot
+     */
+    __host__ __device__ const_iterator end_slot() const noexcept { return slots_ + num_slots_; }
+
+    /**
+     * @brief Returns an iterator to one past the last slot.
+     *
+     * @return An iterator to one past the last slot
+     */
+    __host__ __device__ iterator end_slot() noexcept { return slots_ + num_slots_; }
+  };
+
+ public:
+  /**
+   * @brief Mutable, non-owning view-type that may be used in device code to
+   * perform singular inserts into the filter.
+   *
+   * `device_mutable_view` is trivially-copyable and is intended to be passed by
+   * value.
+   *
+   * Example:
+   * \code{.cpp}
+   * cuco::static_map<int> m{100'000, -6};
+   *
+   * // Inserts a sequence of keys {0, 1, 2, 3}
+   * thrust::for_each(thrust::make_counting_iterator(0),
+   *                  thrust::make_counting_iterator(50'000),
+   *                  [filter = bf.get_mutable_device_view()]
+   *                  __device__ (auto i) mutable {
+   *                     filter.insert(i);
+   *                  });
+   * \endcode
+   */
+  class device_mutable_view : public device_view_base {
+   public:
+    // Import member type definitions from `bloom_filter`
+    using key_type         = Key;                      ///< Key type
+    using slot_type        = slot_type;                ///< Filter slot type
+    using atomic_slot_type = atomic_slot_type;         ///< Filter slot type
+    using iterator         = atomic_slot_type*;        ///< Filter slot iterator type
+    using const_iterator   = atomic_slot_type const*;  ///< Filter slot const iterator type
+
+    /**
+     * @brief Construct a mutable view of the array pointed to by `slots`.
+     *
+     * @param slots Pointer to beginning of the initialized slots array
+     * @param num_bits The total number of bits in the filter
+     * @param num_hashes The number of hashes to be applied to a key
+     */
+    __host__ __device__ device_mutable_view(atomic_slot_type* slots,
+                                            std::size_t num_bits,
+                                            std::size_t num_hashes) noexcept
+      : device_view_base{slots, num_bits, num_hashes}
+    {
+    }
+
+   public:
+    /**
+     * @brief Construct a mutable view of the array pointed to by `slots` and
+     * initializes the slot array.
+     *
+     * @tparam CG Type of the cooperative group this operation is executed with
+     * @param g Cooperative group this operation is executed with
+     * @param slots Pointer to beginning of the array used for slot storage
+     * @param num_bits The total number of bits in the filter
+     * @param num_hashes The number of hashes to be applied to a key
+     * @return A device_mutable_view object based on the given parameters
+     */
+    template <typename CG>
+    __device__ static device_mutable_view make_from_uninitialized_slots(
+      CG g, void* const slots, std::size_t num_bits, std::size_t num_hashes) noexcept
+    {
+      device_view_base::initialize_slots(g, reinterpret_cast<atomic_slot_type*>(slots), num_bits);
+      return device_mutable_view{reinterpret_cast<atomic_slot_type*>(slots), num_bits, num_hashes};
+    }
+
+    /**
+     * @brief Inserts the specified key into the filter.
+     *
+     * Returns a `bool` denoting whether the key's signature was not already
+     * present in the slot.
+     *
+     * @tparam Hash1 Unary callable type
+     * @tparam Hash2 Unary callable type
+     * @tparam Hash3 Unary callable type
+     * @param key The key to insert
+     * @param hash1 First hash function; used to determine a filter slot
+     * @param hash2 Second hash function; used to generate a signature of the key
+     * @param hash3 Third hash function; used to generate a signature of the key
+     * @return `true` if the pattern was not already in the filter,
+     * `false` otherwise.
+     */
+    template <typename Hash1 = cuco::detail::MurmurHash3_32<key_type>,
+              typename Hash2 = Hash1,
+              typename Hash3 = Hash2>
+    __device__ bool insert(key_type const& key,
+                           Hash1 hash1 = Hash1{},
+                           Hash2 hash2 = Hash2{1},
+                           Hash3 hash3 = Hash3{2}) noexcept;
+  };  // class device mutable view
+
+  /**
+   * @brief Non-owning view-type that may be used in device code to
+   * perform singular find and contains operations for the filter.
+   *
+   * `device_view` is trivially-copyable and is intended to be passed by
+   * value.
+   *
+   */
+  class device_view : public device_view_base {
+   public:
+    // Import member type definitions from `bloom_filter`
+    using key_type         = Key;                      ///< Key type
+    using slot_type        = slot_type;                ///< Filter slot type
+    using atomic_slot_type = atomic_slot_type;         ///< Filter slot type
+    using iterator         = atomic_slot_type*;        ///< Filter slot iterator type
+    using const_iterator   = atomic_slot_type const*;  ///< Filter slot const iterator type
+
+    /**
+     * @brief Construct a mutable view of the array pointed to by `slots`.
+     *
+     * @param slots Pointer to beginning of the initialized slots array
+     * @param num_bits The total number of bits in the filter
+     * @param num_hashes The number of hashes to be applied to a key
+     */
+    __host__ __device__ device_view(atomic_slot_type* slots,
+                                    std::size_t num_bits,
+                                    std::size_t num_hashes) noexcept
+      : device_view_base{slots, num_bits, num_hashes}
+    {
+    }
+
+    /**
+     * @brief Construct a `device_view` from a `device_mutable_view` object
+     *
+     * @param mutable_filter object of type `device_mutable_view`
+     */
+    __host__ __device__ explicit device_view(device_mutable_view mutable_filter)
+      : device_view_base{mutable_filter.get_slots(),
+                         mutable_filter.get_num_bits(),
+                         mutable_filter.get_num_hashes()}
+    {
+    }
+
+    /**
+     * @brief Makes a copy of given `device_view` using non-owned memory.
+     *
+     * This function is intended to be used to create shared memory copies of
+     * small static filters, although global memory can be used as well.
+     *
+     * Example:
+     * @code{.cpp}
+     * //TODO
+     * @endcode
+     *
+     * @tparam CG The type of the cooperative thread group
+     * @param g The cooperative thread group used to copy the slots
+     * @param source_device_view `device_view` to copy from
+     * @param memory_to_use Array large enough to support `num_slots` slots.
+     * Object does not take the ownership of the memory
+     * @return Copy of passed `device_view`
+     */
+    template <typename CG>
+    __device__ static device_view make_copy(CG g,
+                                            void* const memory_to_use,
+                                            device_view source_device_view) noexcept
+    {
+      atomic_slot_type* const dest_slots      = reinterpret_cast<atomic_slot_type*>(memory_to_use);
+      atomic_slot_type const* const src_slots = source_device_view.get_slots();
+
+#if defined(CUDA_HAS_CUDA_BARRIER)
+      __shared__ cuda::barrier<cuda::thread_scope::thread_scope_block> barrier;
+      if (g.thread_rank() == 0) { init(&barrier, g.size()); }
+      g.sync();
+
+      cuda::memcpy_async(g,
+                         dest_slots,
+                         src_slots,
+                         sizeof(atomic_slot_type) * source_device_view.get_num_slots(),
+                         barrier);
+
+      barrier.arrive_and_wait();
+#else
+      for (std::size_t i = g.thread_rank(); i < source_device_view.get_num_slots(); i += g.size()) {
+        new (&dest_slots[i]) atomic_slot_type{src_slots[i].load(cuda::memory_order_relaxed)};
+      }
+      g.sync();
+#endif
+
+      return device_view(
+        dest_slots, source_device_view.get_num_bits(), source_device_view.get_num_hashes());
+    }
+
+    /**
+     * @brief Indicates whether the key's signature is present in the filter.
+     *
+     * If the siganture of the key `k` was inserted into the filter, `contains`
+     * returns `true`. Otherwise, it returns `false`.
+     *
+     * @tparam Hash1 Unary callable type
+     * @tparam Hash2 Unary callable type
+     * @tparam Hash3 Unary callable type
+     * @param k The key to search for
+     * @param hash1 First hash function; used to determine a filter slot
+     * @param hash2 Second hash function; used to generate a signature of the key
+     * @param hash3 Third hash function; used to generate a signature of the key
+     * @return A boolean indicating whether the key's signature is present in
+     * the filter.
+     */
+    template <typename Hash1 = cuco::detail::MurmurHash3_32<key_type>,
+              typename Hash2 = Hash1,
+              typename Hash3 = Hash2>
+    __device__ bool contains(Key const& k,
+                             Hash1 hash1 = Hash1{},
+                             Hash2 hash2 = Hash2{1},
+                             Hash3 hash3 = Hash3{2}) const noexcept;
+  };
+
+  /**
+   * @brief Constructs a device_view object based on the members of the `bloom_filter` object.
+   *
+   * @return A device_view object based on the members of the `bloom_filter` object
+   */
+  device_view get_device_view() const noexcept
+  {
+    return device_view(slots_, num_bits_, num_hashes_);
+  }
+
+  /**
+   * @brief Constructs a device_mutable_view object based on the members of the `bloom_filter`
+   * object
+   *
+   * @return A device_mutable_view object based on the members of the `bloom_filter` object
+   */
+  device_mutable_view get_device_mutable_view() const noexcept
+  {
+    return device_mutable_view(slots_, num_bits_, num_hashes_);
+  }
+
+ private:
+  atomic_slot_type* slots_{nullptr};      ///< Pointer to flat slot storage
+  std::size_t num_bits_{};                ///< Total number of bits in the filter
+  std::size_t num_slots_{};               ///< Total number of slots in the filter
+  std::size_t num_hashes_{};              ///< Number of hash functions to apply (k)
+  slot_allocator_type slot_allocator_{};  ///< Allocator used to allocate slots
+};
+}  // namespace cuco
+
+#include <cuco/detail/bloom_filter.inl>
diff --git a/include/cuco/detail/__config b/include/cuco/detail/__config
index 197354a4f..d7764a09f 100644
--- a/include/cuco/detail/__config
+++ b/include/cuco/detail/__config
@@ -27,4 +27,9 @@
 
 #if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 700)
 #define CUCO_HAS_INDEPENDENT_THREADS
+#endif
+
+#if defined(CUDART_VERSION) && (CUDART_VERSION >= 11500) && defined(__CUDA_ARCH__) && \
+(__CUDA_ARCH__ >= 700)
+#define CUCO_HAS_CUDA_ANNOTATED_PTR
 #endif
\ No newline at end of file
diff --git a/include/cuco/detail/bloom_filter.inl b/include/cuco/detail/bloom_filter.inl
new file mode 100644
index 000000000..e1bfa45b4
--- /dev/null
+++ b/include/cuco/detail/bloom_filter.inl
@@ -0,0 +1,151 @@
+/*
+ * Copyright (c) 2021-2022, 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.
+ */
+
+#include <cuco/detail/hash_functions.cuh>
+#include <cuco/detail/utils.hpp>
+
+#include <cuda/std/atomic>
+
+#include <algorithm>
+#include <cstddef>
+#include <iterator>
+#include <memory>
+
+namespace cuco {
+
+template <typename Key, cuda::thread_scope Scope, typename Allocator, typename Slot>
+bloom_filter<Key, Scope, Allocator, Slot>::bloom_filter(std::size_t num_bits,
+                                                        std::size_t num_hashes,
+                                                        Allocator const& alloc,
+                                                        cudaStream_t stream)
+  : num_bits_{SDIV(std::max(std::size_t{1}, num_bits), detail::type_bits<slot_type>()) *
+              detail::type_bits<slot_type>()},
+    num_slots_{SDIV(std::max(std::size_t{1}, num_bits), detail::type_bits<slot_type>())},
+    num_hashes_{std::clamp(num_hashes, std::size_t{1}, detail::type_bits<slot_type>())},
+    slot_allocator_{alloc}
+{
+  slots_ = std::allocator_traits<slot_allocator_type>::allocate(slot_allocator_, num_slots_);
+
+  initialize(stream);
+}
+
+template <typename Key, cuda::thread_scope Scope, typename Allocator, typename Slot>
+bloom_filter<Key, Scope, Allocator, Slot>::~bloom_filter()
+{
+  std::allocator_traits<slot_allocator_type>::deallocate(slot_allocator_, slots_, num_slots_);
+}
+
+template <typename Key, cuda::thread_scope Scope, typename Allocator, typename Slot>
+void bloom_filter<Key, Scope, Allocator, Slot>::initialize(cudaStream_t stream)
+{
+  std::size_t constexpr block_size = 256;
+  std::size_t constexpr stride     = 4;
+  std::size_t const grid_size      = SDIV(num_slots_, stride * block_size);
+
+  detail::initialize<block_size><<<grid_size, block_size, 0, stream>>>(slots_, num_slots_);
+}
+
+template <typename Key, cuda::thread_scope Scope, typename Allocator, typename Slot>
+template <typename InputIt, typename Hash1, typename Hash2, typename Hash3>
+void bloom_filter<Key, Scope, Allocator, Slot>::insert(
+  InputIt first, InputIt last, cudaStream_t stream, Hash1 hash1, Hash2 hash2, Hash3 hash3)
+{
+  auto num_keys = std::distance(first, last);
+  if (num_keys == 0) { return; }
+
+  std::size_t constexpr block_size = 256;
+  std::size_t constexpr stride     = 4;
+  std::size_t const grid_size      = SDIV(num_keys, stride * block_size);
+  detail::insert<block_size><<<grid_size, block_size, 0, stream>>>(
+    first, last, get_device_mutable_view(), hash1, hash2, hash3);
+}
+
+template <typename Key, cuda::thread_scope Scope, typename Allocator, typename Slot>
+template <typename InputIt, typename OutputIt, typename Hash1, typename Hash2, typename Hash3>
+void bloom_filter<Key, Scope, Allocator, Slot>::contains(InputIt first,
+                                                         InputIt last,
+                                                         OutputIt output_begin,
+                                                         cudaStream_t stream,
+                                                         Hash1 hash1,
+                                                         Hash2 hash2,
+                                                         Hash3 hash3)
+{
+  auto num_keys = std::distance(first, last);
+  if (num_keys == 0) { return; }
+
+  std::size_t constexpr block_size = 256;
+  std::size_t constexpr stride     = 4;
+  std::size_t const grid_size      = SDIV(num_keys, stride * block_size);
+  detail::contains<block_size><<<grid_size, block_size, 0, stream>>>(
+    first, last, output_begin, get_device_view(), hash1, hash2, hash3);
+}
+
+template <typename Key, cuda::thread_scope Scope, typename Allocator, typename Slot>
+template <typename Hash1, typename Hash2>
+__device__ Slot bloom_filter<Key, Scope, Allocator, Slot>::device_view_base::key_pattern(
+  Key const& key, Hash1 hash1, Hash2 hash2) const noexcept
+{
+  slot_type pattern = 0;
+  std::size_t k     = 0;
+  std::size_t i     = 0;
+
+  auto h1 = hash1(key);
+  // odd number to be co-prime with the number of bits in the slot
+  auto h2 = hash2(key) | 1;
+
+  while (k < num_hashes_) {
+    // extended double hashing
+    slot_type const bit =
+      slot_type{1} << ((h1 + (i * h2) + ((i * i * i - i) / 6)) % detail::type_bits<slot_type>());
+
+    if (not(pattern & bit)) {
+      pattern += bit;
+      k++;
+    }
+    i++;
+  }
+
+  return pattern;
+}
+
+template <typename Key, cuda::thread_scope Scope, typename Allocator, typename Slot>
+template <typename Hash1, typename Hash2, typename Hash3>
+__device__ bool bloom_filter<Key, Scope, Allocator, Slot>::device_mutable_view::insert(
+  Key const& key, Hash1 hash1, Hash2 hash2, Hash3 hash3) noexcept
+{
+  auto slot          = key_slot(key, hash1);
+  auto const pattern = key_pattern(key, hash2, hash3);
+  auto const result  = slot->fetch_or(pattern, cuda::std::memory_order_relaxed);
+
+  // return `true` if the key's pattern was not already present in the filter,
+  // else return `false`.
+  return (result & pattern) != pattern;
+}
+
+template <typename Key, cuda::thread_scope Scope, typename Allocator, typename Slot>
+template <typename Hash1, typename Hash2, typename Hash3>
+__device__ bool bloom_filter<Key, Scope, Allocator, Slot>::device_view::contains(
+  Key const& key, Hash1 hash1, Hash2 hash2, Hash3 hash3) const noexcept
+{
+  auto slot          = key_slot(key, hash1);
+  auto const pattern = key_pattern(key, hash2, hash3);
+  auto const result  = slot->load(cuda::std::memory_order_relaxed);
+
+  // return `true` if the key's pattern was already present in the filter,
+  // else return `false`.
+  return (result & pattern) == pattern;
+}
+}  // namespace cuco
diff --git a/include/cuco/detail/bloom_filter_kernels.cuh b/include/cuco/detail/bloom_filter_kernels.cuh
new file mode 100644
index 000000000..ed65c8c62
--- /dev/null
+++ b/include/cuco/detail/bloom_filter_kernels.cuh
@@ -0,0 +1,124 @@
+/*
+ * Copyright (c) 2021-2022, 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.
+ */
+
+#include <cuda/std/atomic>
+
+#include <cstddef>
+
+namespace cuco {
+namespace detail {
+
+/**
+ * @brief Initializes each slot in the flat `slot` storage.
+ *
+ * @tparam block_size The size of the thread block
+ * @tparam atomic_slot_type Type of the slot's atomic container
+ * @param slots Pointer to flat `slot` storage
+ * @param num_slots Size of the storage pointed to by `slots`
+ */
+template <std::size_t block_size, typename atomic_slot_type>
+__global__ void __launch_bounds__(block_size)
+  initialize(atomic_slot_type* const slots, std::size_t num_slots)
+{
+  for (std::size_t tid = block_size * blockIdx.x + threadIdx.x; tid < num_slots;
+       tid += gridDim.x * block_size) {
+    new (&slots[tid]) atomic_slot_type{0};
+  }
+}
+
+/**
+ * @brief Inserts all keys in the range `[first, last)`.
+ *
+ * @tparam block_size The size of the thread block
+ * @tparam InputIt Device accessible input iterator whose `value_type` is
+ * convertible to the filter's `key_type`
+ * @tparam View Type of device view allowing access of filter storage
+ * @tparam Hash Unary callable type
+ * @param first Beginning of the sequence of keys
+ * @param last End of the sequence of keys
+ * @param view Mutable device view used to access the filter's slot storage
+ * @param hash1 First hash function; used to determine a filter slot
+ * @param hash2 Second hash function; used to generate a signature of the key
+ * @param hash3 Third hash function; used to generate a signature of the key
+ */
+template <std::size_t block_size,
+          typename InputIt,
+          typename View,
+          typename Hash1,
+          typename Hash2,
+          typename Hash3>
+__global__ void __launch_bounds__(block_size)
+  insert(InputIt first, InputIt last, View view, Hash1 hash1, Hash2 hash2, Hash3 hash3)
+{
+  std::size_t tid = block_size * blockIdx.x + threadIdx.x;
+  auto it         = first + tid;
+
+  while (it < last) {
+    typename View::key_type const key{*it};
+    view.insert(key, hash1, hash2, hash3);
+    it += gridDim.x * block_size;
+  }
+}
+
+/**
+ * @brief Indicates whether the keys in the range `[first, last)` are contained
+ * in the filter.
+ *
+ * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists
+ * in the filter.
+ *
+ * @tparam block_size The size of the thread block
+ * @tparam InputIt Device accessible input iterator whose `value_type` is
+ * convertible to the filter's `key_type`
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
+ * convertible to `bool`.
+ * @tparam View Type of device view allowing access of filter storage
+ * @tparam Hash Unary callable type
+ * @param first Beginning of the sequence of keys
+ * @param last End of the sequence of keys
+ * @param output_begin Beginning of the sequence of booleans for the presence of each key
+ * @param view Mutable device view used to access the filter's slot storage
+ * @param hash1 First hash function; used to determine a filter slot
+ * @param hash2 Second hash function; used to generate a signature of the key
+ * @param hash3 Third hash function; used to generate a signature of the key
+ */
+template <std::size_t block_size,
+          typename InputIt,
+          typename OutputIt,
+          typename View,
+          typename Hash1,
+          typename Hash2,
+          typename Hash3>
+__global__ void __launch_bounds__(block_size) contains(InputIt first,
+                                                       InputIt last,
+                                                       OutputIt output_begin,
+                                                       View view,
+                                                       Hash1 hash1,
+                                                       Hash2 hash2,
+                                                       Hash3 hash3)
+{
+  std::size_t tid = block_size * blockIdx.x + threadIdx.x;
+  auto it         = first + tid;
+
+  while ((first + tid) < last) {
+    typename View::key_type const key{*(first + tid)};
+    *(output_begin + tid) = view.contains(key, hash1, hash2, hash3);
+    tid += gridDim.x * block_size;
+  }
+}
+
+}  // namespace detail
+}  // namespace cuco
diff --git a/include/cuco/detail/cache_residency_control.cuh b/include/cuco/detail/cache_residency_control.cuh
new file mode 100644
index 000000000..f5711e631
--- /dev/null
+++ b/include/cuco/detail/cache_residency_control.cuh
@@ -0,0 +1,90 @@
+/*
+ * Copyright (c) 2022, 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
+ */
+
+#pragma once
+
+#include <algorithm>
+#include <iterator>
+
+namespace cuco {
+
+/**
+ * @brief Registers the global memory region `[begin, end)` to
+ * be permanently resident in L2 cache.
+ *
+ * @tparam Iterator Accessor of the memory region
+ * @param[in, out] stream The CUDA stream this region is accessed through
+ * @param[in] begin Start of the memory region to be mapped
+ * @param[in] end End of the memory region
+ * @param[in] hit_rate Probability for a sub-segment to be mapped in L2
+ * @param[in] carve_out Fraction of total L2 space to be blocked for resident memory segments
+ *
+ * @note Only has effect on Ampere and above.
+ * @note Assumes the memory region to be contiguous.
+ */
+template <typename Iterator>
+void register_l2_persistence(
+  cudaStream_t& stream, Iterator begin, Iterator end, float hit_rate = 0.6f, float carve_out = 1.0f)
+{
+  using value_type = typename std::iterator_traits<Iterator>::value_type;
+
+  int device_id;
+  cudaGetDevice(&device_id);
+  cudaDeviceProp prop;
+  cudaGetDeviceProperties(&prop, device_id);
+
+  hit_rate  = std::clamp(hit_rate, 0.0f, 1.0f);
+  carve_out = std::clamp(carve_out, 0.0f, 1.0f);
+  // Must be less than cudaDeviceProp::accessPolicyMaxWindowSize
+  auto const num_bytes = std::min(std::distance(begin, end) * sizeof(value_type),
+                                  std::size_t(prop.accessPolicyMaxWindowSize));
+
+  cudaDeviceSetLimit(cudaLimitPersistingL2CacheSize, carve_out * prop.persistingL2CacheMaxSize);
+
+  // Stream level attributes data structure
+  cudaStreamAttrValue stream_attribute;
+  // Global Memory data pointer
+  stream_attribute.accessPolicyWindow.base_ptr = reinterpret_cast<void*>(&begin[0]);
+  // Number of bytes for persistence access.
+  stream_attribute.accessPolicyWindow.num_bytes = num_bytes;
+  // Hint for cache hit ratio
+  stream_attribute.accessPolicyWindow.hitRatio = hit_rate;
+  // Type of access property on cache hit
+  stream_attribute.accessPolicyWindow.hitProp = cudaAccessPropertyPersisting;
+  // Type of access property on cache miss.
+  stream_attribute.accessPolicyWindow.missProp = cudaAccessPropertyStreaming;
+  // Set the attributes to a CUDA stream of type cudaStream_t
+  cudaStreamSetAttribute(stream, cudaStreamAttributeAccessPolicyWindow, &stream_attribute);
+}
+
+/**
+ * @brief Globally removes all persistent cache lines from L2.
+ *
+ * @param[in, out] stream The CUDA stream the resident region has been accessed through
+ *
+ * @note Only has effect on Ampere and above.
+ */
+void unregister_l2_persistence(cudaStream_t& stream)
+{
+  cudaStreamAttrValue stream_attribute;
+  // Setting the window size to 0 to disable it
+  stream_attribute.accessPolicyWindow.num_bytes = 0;
+  // Overwrite the access policy attribute of CUDA Stream
+  cudaStreamSetAttribute(stream, cudaStreamAttributeAccessPolicyWindow, &stream_attribute);
+  // Remove any persistent lines in L2$
+  cudaCtxResetPersistingL2Cache();
+}
+
+}  // namespace cuco
\ No newline at end of file
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 92dd5a34d..19ce6a429 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -64,6 +64,7 @@ ConfigureTest(STATIC_MAP_TEST
     static_map/stream_test.cu
     static_map/unique_sequence_test.cu)
 
+####################################################################################################
 foreach(arch IN LISTS CMAKE_CUDA_ARCHITECTURES)
     if("${arch}" MATCHES "^6")
       target_compile_definitions(STATIC_MAP_TEST PRIVATE CUCO_NO_INDEPENDENT_THREADS)
@@ -86,3 +87,8 @@ ConfigureTest(STATIC_MULTIMAP_TEST
     static_multimap/multiplicity_test.cu
     static_multimap/non_match_test.cu
     static_multimap/pair_function_test.cu)
+
+###################################################################################################
+# - bloom_filter tests -------------------------------------------------------------------------
+ConfigureTest(BLOOM_FILTER_TEST
+bloom_filter/bloom_filter_test.cu)
\ No newline at end of file
diff --git a/tests/bloom_filter/bloom_filter_test.cu b/tests/bloom_filter/bloom_filter_test.cu
new file mode 100644
index 000000000..1c7e4f608
--- /dev/null
+++ b/tests/bloom_filter/bloom_filter_test.cu
@@ -0,0 +1,183 @@
+/*
+ * Copyright (c) 2021-2022, 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.
+ */
+
+#include <utils.hpp>
+
+#include <cuco/bloom_filter.cuh>
+
+#include <thrust/device_vector.h>
+#include <thrust/sequence.h>
+
+#include <catch2/catch.hpp>
+
+template <typename Key, typename Slot, std::size_t NumSlots>
+__global__ void shared_memory_filter_kernel(bool* key_found)
+{
+  namespace cg = cooperative_groups;
+
+  using filter_type =
+    cuco::bloom_filter<Key, cuda::thread_scope_block, cuco::cuda_allocator<char>, Slot>;
+  using mutable_view_type = typename filter_type::device_mutable_view;
+  using view_type         = typename filter_type::device_view;
+
+  __shared__ typename mutable_view_type::slot_type slots[NumSlots];
+
+  auto mutable_view = mutable_view_type::make_from_uninitialized_slots(
+    cg::this_thread_block(), &slots[0], NumSlots * CHAR_BIT, 4);
+
+  auto g            = cg::this_thread_block();
+  std::size_t index = threadIdx.x + blockIdx.x * blockDim.x;
+  int rank          = g.thread_rank();
+
+  mutable_view.insert(rank);
+  g.sync();
+
+  auto view        = view_type(mutable_view);
+  key_found[index] = view.contains(rank);
+}
+
+TEMPLATE_TEST_CASE_SIG("Unit tests for cuco::bloom_filter.",
+                       "",
+                       ((typename Key, typename Slot), Key, Slot),
+                       (int32_t, int32_t),
+                       (int64_t, int64_t))
+{
+  using filter_type =
+    cuco::bloom_filter<Key, cuda::thread_scope_device, cuco::cuda_allocator<char>, Slot>;
+
+  SECTION("Edge cases during object construction.")
+  {
+    SECTION(
+      "The ctor should allocate at least a single slot independent of the value given by num_bits.")
+    {
+      filter_type filter{0, 1};
+
+      REQUIRE(filter.get_num_slots() == 1);
+      REQUIRE(filter.get_num_bits() == sizeof(Slot) * CHAR_BIT);
+    }
+
+    SECTION("The number of hash function to apply should always be in range [1, slot bits].")
+    {
+      filter_type filter_a{1, 0};
+
+      REQUIRE(filter_a.get_num_hashes() == 1);
+
+      filter_type filter_b{1, 1000};
+      REQUIRE(filter_b.get_num_hashes() == sizeof(Slot) * CHAR_BIT);
+    }
+  }
+
+  SECTION("Core functionality.")
+  {
+    std::size_t constexpr num_keys{10'000'000};
+    std::size_t constexpr num_bits{250'000'000};
+    std::size_t constexpr num_hashes{4};
+
+    // generate test data
+    thrust::device_vector<Key> keys(num_keys * 2);
+    thrust::sequence(keys.begin(), keys.end(), 1);
+    thrust::device_vector<bool> contained(num_keys, false);
+
+    // true-positives
+    auto tp_begin = keys.begin();
+    auto tp_end   = tp_begin + num_keys;
+
+    filter_type filter{num_bits, num_hashes};
+
+    SECTION("There should be no keys present in an empty filter.")
+    {
+      filter.contains(tp_begin, tp_end, contained.begin());
+
+      REQUIRE(cuco::test::none_of(
+        contained.begin(), contained.end(), [] __device__(bool const& b) { return b; }));
+    }
+
+    SECTION("Host-side bulk API.")
+    {
+      filter.insert(tp_begin, tp_end);
+
+      SECTION("All inserted keys should be present in the filter after insertion.")
+      {
+        filter.contains(tp_begin, tp_end, contained.begin());
+
+        REQUIRE(cuco::test::all_of(
+          contained.begin(), contained.end(), [] __device__(bool const& b) { return b; }));
+      }
+
+      SECTION(
+        "Only a fraction of foreign keys (false positives) should be contained in the filter.")
+      {
+        // true negatives
+        auto tn_begin = tp_end;
+        auto tn_end   = keys.end();
+
+        filter.contains(tn_begin, tn_end, contained.begin());
+
+        float fp  = thrust::count(thrust::device, contained.begin(), contained.end(), true);
+        float fpr = fp / num_keys;
+        REQUIRE(fpr < 0.05);
+      }
+
+      SECTION("Re-initializing the filter should delete all keys.")
+      {
+        filter.initialize();
+
+        filter.contains(tp_begin, tp_end, contained.begin());
+
+        REQUIRE(cuco::test::none_of(
+          contained.begin(), contained.end(), [] __device__(bool const& b) { return b; }));
+      }
+    }
+
+    SECTION("Device-side API.")
+    {
+      SECTION("Insert keys using the filter's mutable view.")
+      {
+        auto view = filter.get_device_mutable_view();
+
+        thrust::for_each(
+          thrust::device, tp_begin, tp_end, [view] __device__(Key const& key) mutable {
+            view.insert(key);
+          });
+
+        filter.contains(tp_begin, tp_end, contained.begin());
+
+        REQUIRE(cuco::test::all_of(
+          contained.begin(), contained.end(), [] __device__(bool const& b) { return b; }));
+      }
+
+      SECTION("Check if all inserted keys can be found using the filter's device view.")
+      {
+        filter.insert(tp_begin, tp_end);
+
+        auto view = filter.get_device_view();
+
+        REQUIRE(cuco::test::all_of(
+          tp_begin, tp_end, [view] __device__(Key const& key) { return view.contains(key); }));
+      }
+    }
+  }
+
+  SECTION("Filter in shared memory.")
+  {
+    thrust::device_vector<bool> contained(1024, false);
+
+    shared_memory_filter_kernel<Key, Slot, 2048><<<1, 1024>>>(contained.data().get());
+
+    REQUIRE(cuco::test::all_of(
+      contained.begin(), contained.end(), [] __device__(bool const& b) { return b; }));
+  }
+}
\ No newline at end of file