diff --git a/example/ck_tile/99_toy_example/01_add/CMakeLists.txt b/example/ck_tile/99_toy_example/01_add/CMakeLists.txt
index f241173fe7..89d35de072 100644
--- a/example/ck_tile/99_toy_example/01_add/CMakeLists.txt
+++ b/example/ck_tile/99_toy_example/01_add/CMakeLists.txt
@@ -1,9 +1,9 @@
-set(EXAMPLE_REDUCE "add")
+set(EXAMPLE_REDUCE "add_3D")
 # not using add_example_executable() to add this target, since we don't want this to have
 # to be included in "make all/install/check"
 message("adding example ${EXAMPLE_REDUCE}")
 
-add_executable(${EXAMPLE_REDUCE} EXCLUDE_FROM_ALL add.cpp)
+add_executable(${EXAMPLE_REDUCE} EXCLUDE_FROM_ALL add_3D.cpp)
 target_include_directories(${EXAMPLE_REDUCE} PRIVATE ${CMAKE_CURRENT_LIST_DIR})
 set(EXAMPLE_REDUCE_COMPILE_OPTIONS)
 
diff --git a/example/ck_tile/99_toy_example/01_add/add_3D.cpp b/example/ck_tile/99_toy_example/01_add/add_3D.cpp
new file mode 100644
index 0000000000..d9fb10b0a4
--- /dev/null
+++ b/example/ck_tile/99_toy_example/01_add/add_3D.cpp
@@ -0,0 +1,117 @@
+#include "ck_tile/host.hpp"
+#include "reference_add_3D.hpp"
+#include "add_3D.hpp"
+#include <cstring>
+
+auto create_args(int argc, char* argv[])
+{
+    ck_tile::ArgParser arg_parser;
+    arg_parser.insert("b", "4", "b dimension")
+        .insert("m", "10240", "m dimension")
+        .insert("n", "4096", "n dimension")
+        .insert("v", "1", "cpu validation or not")
+        .insert("prec", "fp16", "precision")
+        .insert("warmup", "200", "cold iter")
+        .insert("repeat", "1000", "hot iter");
+
+    bool result = arg_parser.parse(argc, argv);
+    return std::make_tuple(result, arg_parser);
+}
+
+template <typename DataType>
+bool run(const ck_tile::ArgParser& arg_parser)
+{
+    using XDataType       = DataType;
+    using ComputeDataType = float;
+    using YDataType       = DataType;
+
+    ck_tile::index_t b = arg_parser.get_int("b");
+    ck_tile::index_t m = arg_parser.get_int("m");
+    ck_tile::index_t n = arg_parser.get_int("n");
+    int do_validation  = arg_parser.get_int("v");
+    int warmup         = arg_parser.get_int("warmup");
+    int repeat         = arg_parser.get_int("repeat");
+
+    ck_tile::HostTensor<XDataType> x_host_a({b, m, n});
+    ck_tile::HostTensor<XDataType> x_host_b({b, m, n});
+
+    ck_tile::HostTensor<YDataType> y_host_ref({b, m, n});
+    ck_tile::HostTensor<YDataType> y_host_dev({b, m, n});
+
+    ck_tile::FillUniformDistribution<XDataType>{-5.f, 5.f}(x_host_a);
+    ck_tile::FillUniformDistribution<XDataType>{-5.f, 5.f}(x_host_b);
+
+    ck_tile::DeviceMem x_buf_a(x_host_a.get_element_space_size_in_bytes());
+    ck_tile::DeviceMem x_buf_b(x_host_b.get_element_space_size_in_bytes());
+    ck_tile::DeviceMem y_buf(y_host_dev.get_element_space_size_in_bytes());
+
+    x_buf_a.ToDevice(x_host_a.data());
+    x_buf_b.ToDevice(x_host_b.data());
+
+    using BlockWarps =
+        ck_tile::sequence<1, 8>; // number of concurrent warps in one block (if 8 warps * 64 threads
+                                 // per warp, 512 threads in one block are NEEDED)
+    using BlockTile =
+        ck_tile::sequence<1, 4096>; // shape of one blockTile (elements covered by one block)
+    using WarpTile = ck_tile::sequence<1, 512>; // shape of one warpTile (elements covered by one
+                                                // warp (64 threads))
+    using Vector = ck_tile::sequence<1, 8>; // shape of one vector (elements covered by one thread)
+
+    constexpr ck_tile::index_t kBlockSize =
+        512; // number of blockWarps * number of threads per warp
+    constexpr ck_tile::index_t kBlockPerCu = 1;
+    ck_tile::index_t kGridSize             = (b * m / BlockTile::at(ck_tile::number<0>{}));
+    std::cout << "block x-size = " << BlockTile::at(ck_tile::number<0>{}) << std::endl;
+    std::cout << "grid size " << kGridSize << std::endl;
+
+    using Shape   = ck_tile::AddShape<BlockWarps, BlockTile, WarpTile, Vector>;
+    using Porblem = ck_tile::AddProblem<XDataType, ComputeDataType, YDataType, Shape>;
+
+    using Kernel = ck_tile::Add<Porblem>;
+
+    float ave_time = launch_kernel(ck_tile::stream_config{nullptr, true, 0, warmup, repeat},
+                                   ck_tile::make_kernel<kBlockSize, kBlockPerCu>(
+                                       Kernel{},
+                                       kGridSize,
+                                       kBlockSize,
+                                       0,
+                                       static_cast<XDataType*>(x_buf_a.GetDeviceBuffer()),
+                                       static_cast<XDataType*>(x_buf_b.GetDeviceBuffer()),
+                                       static_cast<YDataType*>(y_buf.GetDeviceBuffer()),
+                                       b,
+                                       m,
+                                       n));
+
+    std::size_t num_btype = 2 * sizeof(XDataType) * b * m * n + sizeof(YDataType) * b * m * n;
+
+    float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+    std::cout << "Perf: " << ave_time << " ms, " << gb_per_sec << " GB/s" << std::endl;
+
+    bool pass = true;
+
+    if(do_validation)
+    {
+        ck_tile::reference_add<XDataType, YDataType>(x_host_a, x_host_b, y_host_ref);
+        y_buf.FromDevice(y_host_dev.mData.data());
+        pass = ck_tile::check_err(y_host_dev, y_host_ref);
+
+        std::cout << "valid:" << (pass ? "y" : "n") << std::flush << std::endl;
+    }
+
+    return pass;
+}
+
+int main(int argc, char* argv[])
+{
+    auto [result, arg_parser] = create_args(argc, argv);
+    if(!result)
+        return -1;
+
+    const std::string data_type = arg_parser.get_str("prec");
+
+    if(data_type == "fp16")
+    {
+        return run<ck_tile::half_t>(arg_parser) ? 0 : -2;
+    }
+}
diff --git a/example/ck_tile/99_toy_example/01_add/add_3D.hpp b/example/ck_tile/99_toy_example/01_add/add_3D.hpp
new file mode 100644
index 0000000000..9ba2d0d823
--- /dev/null
+++ b/example/ck_tile/99_toy_example/01_add/add_3D.hpp
@@ -0,0 +1,177 @@
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck_tile/core.hpp"
+#include "ck_tile/ops/common.hpp"
+
+namespace ck_tile {
+
+template <typename BlockWarps, // num warps along seq<M, N>
+          typename BlockTile,  // block size, seq<M, N>
+          typename WarpTile,   // warp size, seq<M, N>
+          typename Vector>     // contiguous pixels(vector size) along seq<M, N>
+struct AddShape
+{
+    static constexpr index_t Block_M = BlockTile::at(number<0>{}); // elements along M in one Block
+    static constexpr index_t Block_N = BlockTile::at(number<1>{}); // elements along N in one Block
+
+    static constexpr index_t Warp_M = WarpTile::at(number<0>{}); // elements along M in one Warp
+    static constexpr index_t Warp_N = WarpTile::at(number<1>{}); // elements along N in one Warp
+
+    static constexpr index_t Vector_M = Vector::at(number<0>{}); // elements along M in one Vector
+    static constexpr index_t Vector_N = Vector::at(number<1>{}); // elements along N in one Vector
+
+    static constexpr index_t WarpPerBlock_M =
+        BlockWarps::at(number<0>{}); // num concurrent warps along M
+    static constexpr index_t WarpPerBlock_N =
+        BlockWarps::at(number<1>{}); // num concurrent warps along N
+
+    static constexpr index_t ThreadPerWarp_M =
+        Warp_M /
+        Vector_M; // num threads along M in one Warp (ThreadPerWarp_M * ThreadPerWarp_N must be 64)
+    static constexpr index_t ThreadPerWarp_N =
+        Warp_N /
+        Vector_N; // num threads along N in one Warp (ThreadPerWarp_M * ThreadPerWarp_N must be 64)
+
+    static constexpr index_t Repeat_M =
+        Block_M /
+        (WarpPerBlock_M *
+         Warp_M); // num of time a warp iterates along M to ensure the entire block is covered
+    static constexpr index_t Repeat_N =
+        Block_N /
+        (WarpPerBlock_N *
+         Warp_N); // num of time a warp iterates along N to ensure the entire block is covered
+
+    static constexpr index_t BlockSize =
+        warpSize *
+        reduce_on_sequence(BlockWarps{}, multiplies{}, number<1>{}); // num of threads in one block
+};
+
+template <typename XDataType_, typename ComputeDataType_, typename YDataType_, typename BlockShape_>
+struct AddProblem
+{
+    using XDataType       = remove_cvref_t<XDataType_>;       // data type of input tensor
+    using ComputeDataType = remove_cvref_t<ComputeDataType_>; // data type of compute tensor
+    using YDataType       = remove_cvref_t<YDataType_>;       // data type of output tensor
+    using BlockShape      = remove_cvref_t<BlockShape_>;      // block shapes and sizes
+};
+
+struct AddDefaultPolicy
+{
+    template <typename Problem>
+    CK_TILE_DEVICE static constexpr auto MakeXBlockTileDistribution()
+    {
+        using S = typename Problem::BlockShape;
+        return make_static_tile_distribution(
+            tile_distribution_encoding<
+                sequence<>,
+                tuple<sequence<S::Repeat_M,
+                               S::WarpPerBlock_M,
+                               S::ThreadPerWarp_M,
+                               S::Vector_M>, // how many sub division is a block divided in
+                      sequence<S::Repeat_N,
+                               S::WarpPerBlock_N,
+                               S::ThreadPerWarp_N,
+                               S::Vector_N>>, // how many sub division is a block divided in
+                tuple<sequence<1, 2>, sequence<1, 2>>, // What are the shapes of those sub divisions
+                tuple<sequence<1, 1>, sequence<2, 2>>, // What are the shapes of those sub divisions
+                sequence<1, 1, 2, 2>, // How much data does a thread work on and how many iterations
+                                      // of warps are there
+                sequence<0, 3, 0, 3>>{}); // How much data does a thread work on and how many
+                                          // iterations of warps are there
+    }
+};
+
+template <typename Problem_, typename Policy_ = AddDefaultPolicy>
+struct Add
+{
+    using Problem = ck_tile::remove_cvref_t<Problem_>;
+    using Policy  = ck_tile::remove_cvref_t<Policy_>;
+
+    using XDataType       = ck_tile::remove_cvref_t<typename Problem::XDataType>;
+    using ComputeDataType = ck_tile::remove_cvref_t<typename Problem::ComputeDataType>;
+    using YDataType       = ck_tile::remove_cvref_t<typename Problem::YDataType>;
+
+    CK_TILE_DEVICE void operator()(
+        const XDataType* p_x_a, const XDataType* p_x_b, YDataType* p_y, index_t B, index_t M, index_t N) const
+    {
+        using S = typename Problem::BlockShape;
+
+        // Create flattened 2D view by combining B and M dimensions
+        const index_t M_flattened = B * M;
+    
+        const auto x_m_n_a = make_naive_tensor_view<address_space_enum::global,
+                                                    memory_operation_enum::set,
+                                                    amd_buffer_coherence_enum::slc>(
+            p_x_a,
+            make_tuple(M_flattened, N),
+            make_tuple(N, 1),
+            number<S::Vector_N>{},
+            number<1>{}); // raw data, shape of tensor, stride of tensor, lastGarunteedVectorLength,
+                          // lastGarunteedVectorStride
+
+        const auto x_m_n_b = make_naive_tensor_view<address_space_enum::global,
+                                                    memory_operation_enum::set,
+                                                    amd_buffer_coherence_enum::slc>(
+            p_x_b, 
+            make_tuple(M_flattened, N), 
+            make_tuple(N, 1), 
+            number<S::Vector_N>{}, 
+            number<1>{});
+
+        const auto y_m_n = make_naive_tensor_view<address_space_enum::global,
+                                                  memory_operation_enum::set,
+                                                  amd_buffer_coherence_enum::slc>(
+            p_y, 
+            make_tuple(M_flattened, N), 
+            make_tuple(N, 1), 
+            number<S::Vector_N>{}, 
+            number<1>{});
+
+        const auto iM = get_block_id() * S::Block_M; // origin of the block along
+
+        auto x_window_a = make_tile_window(x_m_n_a,
+                                           make_tuple(number<S::Block_M>{}, number<S::Block_N>{}),
+                                           {iM, 0},
+                                           Policy::template MakeXBlockTileDistribution<Problem>());
+
+        auto x_window_b = make_tile_window(x_m_n_b,
+                                           make_tuple(number<S::Block_M>{}, number<S::Block_N>{}),
+                                           {iM, 0},
+                                           Policy::template MakeXBlockTileDistribution<Problem>());
+
+        auto y_window = make_tile_window(y_m_n,
+                                         make_tuple(number<S::Block_M>{}, number<S::Block_N>{}),
+                                         {iM, 0},
+                                         Policy::template MakeXBlockTileDistribution<Problem>());
+
+        index_t num_n_tile_iteration =
+            __builtin_amdgcn_readfirstlane(integer_divide_ceil(N, S::Block_N));
+
+        for(int iN = __builtin_amdgcn_readfirstlane(0); iN < num_n_tile_iteration; ++iN)
+        {
+            const auto xa  = load_tile(x_window_a);
+            const auto xb  = load_tile(x_window_b);
+            auto y_compute = load_tile(y_window);
+
+            constexpr auto spans = decltype(xa)::get_distributed_spans();
+            sweep_tile_span(spans[number<0>{}], [&](auto idx0) {
+                sweep_tile_span(spans[number<1>{}], [&](auto idx1) {
+                    constexpr auto i_j_idx = ck_tile::make_tuple(idx0, idx1);
+                    const auto x           = ck_tile::type_convert<ComputeDataType>(xa[i_j_idx]);
+                    const auto y           = ck_tile::type_convert<ComputeDataType>(xb[i_j_idx]);
+                    y_compute(i_j_idx)     = x + y;
+                });
+            });
+
+            store_tile(y_window, cast_tile<YDataType>(y_compute));
+            move_tile_window(x_window_a, {0, S::Block_N});
+            move_tile_window(x_window_b, {0, S::Block_N});
+            move_tile_window(y_window, {0, S::Block_N});
+        }
+    }
+};
+
+} // namespace ck_tile
diff --git a/example/ck_tile/99_toy_example/01_add/add_3D_use_merge_transform.hpp b/example/ck_tile/99_toy_example/01_add/add_3D_use_merge_transform.hpp
new file mode 100644
index 0000000000..e111d76ff7
--- /dev/null
+++ b/example/ck_tile/99_toy_example/01_add/add_3D_use_merge_transform.hpp
@@ -0,0 +1,206 @@
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck_tile/core.hpp"
+#include "ck_tile/core/tensor/tile_distribution.hpp"
+#include "ck_tile/ops/common.hpp"
+
+namespace ck_tile {
+
+template <typename BlockWarps, // num warps along seq<M, N>
+          typename BlockTile,  // block size, seq<M, N>
+          typename WarpTile,   // warp size, seq<M, N>
+          typename Vector>     // contiguous pixels(vector size) along seq<M, N>
+struct AddShape
+{
+    static constexpr index_t Block_M = BlockTile::at(number<0>{}); // elements along M in one Block
+    static constexpr index_t Block_N = BlockTile::at(number<1>{}); // elements along N in one Block
+
+    static constexpr index_t Warp_M = WarpTile::at(number<0>{}); // elements along M in one Warp
+    static constexpr index_t Warp_N = WarpTile::at(number<1>{}); // elements along N in one Warp
+
+    static constexpr index_t Vector_M = Vector::at(number<0>{}); // elements along M in one Vector
+    static constexpr index_t Vector_N = Vector::at(number<1>{}); // elements along N in one Vector
+
+    static constexpr index_t WarpPerBlock_M =
+        BlockWarps::at(number<0>{}); // num concurrent warps along M
+    static constexpr index_t WarpPerBlock_N =
+        BlockWarps::at(number<1>{}); // num concurrent warps along N
+
+    static constexpr index_t ThreadPerWarp_M =
+        Warp_M /
+        Vector_M; // num threads along M in one Warp (ThreadPerWarp_M * ThreadPerWarp_N must be 64)
+    static constexpr index_t ThreadPerWarp_N =
+        Warp_N /
+        Vector_N; // num threads along N in one Warp (ThreadPerWarp_M * ThreadPerWarp_N must be 64)
+
+    static constexpr index_t Repeat_M =
+        Block_M /
+        (WarpPerBlock_M *
+         Warp_M); // num of time a warp iterates along M to ensure the entire block is covered
+    static constexpr index_t Repeat_N =
+        Block_N /
+        (WarpPerBlock_N *
+         Warp_N); // num of time a warp iterates along N to ensure the entire block is covered
+
+    static constexpr index_t BlockSize =
+        warpSize *
+        reduce_on_sequence(BlockWarps{}, multiplies{}, number<1>{}); // num of threads in one block
+};
+
+template <typename XDataType_, typename ComputeDataType_, typename YDataType_, typename BlockShape_>
+struct AddProblem
+{
+    using XDataType       = remove_cvref_t<XDataType_>;       // data type of input tensor
+    using ComputeDataType = remove_cvref_t<ComputeDataType_>; // data type of compute tensor
+    using YDataType       = remove_cvref_t<YDataType_>;       // data type of output tensor
+    using BlockShape      = remove_cvref_t<BlockShape_>;      // block shapes and sizes
+};
+
+struct AddDefaultPolicy
+{
+    template <typename Problem>
+    CK_TILE_DEVICE static constexpr auto MakeXBlockTileDistribution()
+    {
+        using S = typename Problem::BlockShape;
+        return make_static_tile_distribution(
+            tile_distribution_encoding<
+                sequence<>,
+                tuple<sequence<S::Repeat_M,
+                               S::WarpPerBlock_M,
+                               S::ThreadPerWarp_M,
+                               S::Vector_M>, // how many sub division is a block divided in
+                      sequence<S::Repeat_N,
+                               S::WarpPerBlock_N,
+                               S::ThreadPerWarp_N,
+                               S::Vector_N>>, // how many sub division is a block divided in
+                tuple<sequence<1, 2>, sequence<1, 2>>, // What are the shapes of those sub divisions
+                tuple<sequence<1, 1>, sequence<2, 2>>, // What are the shapes of those sub divisions
+                sequence<1, 1, 2, 2>, // How much data does a thread work on and how many iterations
+                                      // of warps are there
+                sequence<0, 3, 0, 3>>{}); // How much data does a thread work on and how many
+                                          // iterations of warps are there
+    }
+};
+
+template <typename Problem_, typename Policy_ = AddDefaultPolicy>
+struct Add
+{
+    using Problem = ck_tile::remove_cvref_t<Problem_>;
+    using Policy  = ck_tile::remove_cvref_t<Policy_>;
+
+    using XDataType       = ck_tile::remove_cvref_t<typename Problem::XDataType>;
+    using ComputeDataType = ck_tile::remove_cvref_t<typename Problem::ComputeDataType>;
+    using YDataType       = ck_tile::remove_cvref_t<typename Problem::YDataType>;
+
+    CK_TILE_DEVICE void operator()(
+        const XDataType* p_x_a, const XDataType* p_x_b, YDataType* p_y, index_t B, index_t M, index_t N) const
+    {
+        using S = typename Problem::BlockShape;
+
+        // Create 3D tensor views first
+        const auto x_b_m_n_a = make_naive_tensor_view<address_space_enum::global,
+                                                      memory_operation_enum::set,
+                                                      amd_buffer_coherence_enum::slc>(
+            p_x_a,
+            make_tuple(B, M, N),
+            make_tuple(M * N, N, 1),
+            number<S::Vector_N>{},
+            number<1>{});
+
+        const auto x_b_m_n_b = make_naive_tensor_view<address_space_enum::global,
+                                                      memory_operation_enum::set,
+                                                      amd_buffer_coherence_enum::slc>(
+            p_x_b, 
+            make_tuple(B, M, N), 
+            make_tuple(M * N, N, 1), 
+            number<S::Vector_N>{}, 
+            number<1>{});
+
+        const auto y_b_m_n = make_naive_tensor_view<address_space_enum::global,
+                                                    memory_operation_enum::set,
+                                                    amd_buffer_coherence_enum::slc>(
+            p_y, 
+            make_tuple(B, M, N), 
+            make_tuple(M * N, N, 1), 
+            number<S::Vector_N>{}, 
+            number<1>{});
+
+        // Now transform the 3D tensor views to 2D using make_merge_transform
+        // This merges the B and M dimensions
+        const auto x_m_n_a = transform_tensor_descriptor(
+            x_b_m_n_a,
+            make_tuple(
+            make_merge_transform(make_tuple(number<B>{}, number<M>{})),
+            make_pass_through_transform(number<N>{})
+            ),
+            make_tuple(sequence<0, 1>{}, sequence<2>{}),
+            make_tuple(sequence<0>{}, sequence<1>{}));
+
+        const auto x_m_n_b = transform_tensor_descriptor(
+            x_b_m_n_b,
+            make_tuple(
+            make_merge_transform(make_tuple(number<B>{}, number<M>{})),
+            make_pass_through_transform(number<N>{})
+            ),
+            make_tuple(sequence<0, 1>{}, sequence<2>{}),
+            make_tuple(sequence<0>{}, sequence<1>{}));
+
+        const auto y_m_n = transform_tensor_descriptor(
+            y_b_m_n,
+            make_tuple(
+            make_merge_transform(make_tuple(number<B>{}, number<M>{})),
+            make_pass_through_transform(number<N>{})
+            ),
+            make_tuple(sequence<0, 1>{}, sequence<2>{}),
+            make_tuple(sequence<0>{}, sequence<1>{}));
+
+
+
+        const auto iM = get_block_id() * S::Block_M; // origin of the block along
+
+        auto x_window_a = make_tile_window(x_m_n_a,
+                                           make_tuple(number<S::Block_M>{}, number<S::Block_N>{}),
+                                           {iM, 0},
+                                           Policy::template MakeXBlockTileDistribution<Problem>());
+
+        auto x_window_b = make_tile_window(x_m_n_b,
+                                           make_tuple(number<S::Block_M>{}, number<S::Block_N>{}),
+                                           {iM, 0},
+                                           Policy::template MakeXBlockTileDistribution<Problem>());
+
+        auto y_window = make_tile_window(y_m_n,
+                                         make_tuple(number<S::Block_M>{}, number<S::Block_N>{}),
+                                         {iM, 0},
+                                         Policy::template MakeXBlockTileDistribution<Problem>());
+
+        index_t num_n_tile_iteration =
+            __builtin_amdgcn_readfirstlane(integer_divide_ceil(N, S::Block_N));
+
+        for(int iN = __builtin_amdgcn_readfirstlane(0); iN < num_n_tile_iteration; ++iN)
+        {
+            const auto xa  = load_tile(x_window_a);
+            const auto xb  = load_tile(x_window_b);
+            auto y_compute = load_tile(y_window);
+
+            constexpr auto spans = decltype(xa)::get_distributed_spans();
+            sweep_tile_span(spans[number<0>{}], [&](auto idx0) {
+                sweep_tile_span(spans[number<1>{}], [&](auto idx1) {
+                    constexpr auto i_j_idx = ck_tile::make_tuple(idx0, idx1);
+                    const auto x           = ck_tile::type_convert<ComputeDataType>(xa[i_j_idx]);
+                    const auto y           = ck_tile::type_convert<ComputeDataType>(xb[i_j_idx]);
+                    y_compute(i_j_idx)     = x + y;
+                });
+            });
+
+            store_tile(y_window, cast_tile<YDataType>(y_compute));
+            move_tile_window(x_window_a, {0, S::Block_N});
+            move_tile_window(x_window_b, {0, S::Block_N});
+            move_tile_window(y_window, {0, S::Block_N});
+        }
+    }
+};
+
+} // namespace ck_tile
diff --git a/example/ck_tile/99_toy_example/01_add/reference_add_3D.hpp b/example/ck_tile/99_toy_example/01_add/reference_add_3D.hpp
new file mode 100644
index 0000000000..f37f64b37a
--- /dev/null
+++ b/example/ck_tile/99_toy_example/01_add/reference_add_3D.hpp
@@ -0,0 +1,47 @@
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck_tile/core.hpp"
+#include "ck_tile/host/host_tensor.hpp"
+#include <thread>
+
+namespace ck_tile {
+
+template <typename XDataType, typename YDataType>
+CK_TILE_HOST void reference_add(const HostTensor<XDataType>& xa_b_m_n,
+                                const HostTensor<XDataType>& xb_b_m_n,
+                                HostTensor<YDataType>& y_b_m_n)
+{
+    auto f = [&](auto bm_idx) {
+        
+        // Calculate batch and m indices
+        // const int B = xa_b_m_n.mDesc.get_lengths()[0];
+        const int M = xa_b_m_n.mDesc.get_lengths()[1];
+        const int N = xa_b_m_n.mDesc.get_lengths()[2];
+
+        // Convert flat bm_idx to separate b and m indices
+        const int b = bm_idx / M;
+        const int m = bm_idx % M;
+
+        // Process each element in the N dimension
+        for(int n = 0; n < N; ++n)
+        {
+            y_b_m_n(b, m, n) = ck_tile::type_convert<YDataType>(xa_b_m_n(b, m, n)) +
+                              ck_tile::type_convert<YDataType>(xb_b_m_n(b, m, n));
+        }
+
+        
+    };
+
+    // Get total elements to process in the B and M dimensions
+    const int total_bm = y_b_m_n.mDesc.get_lengths()[0] * y_b_m_n.mDesc.get_lengths()[1];
+    
+    // Parallelize computation across the flattened B×M space
+    make_ParallelTensorFunctor(f, total_bm)(std::thread::hardware_concurrency());
+
+    
+}
+
+} // namespace ck_tile