diff --git a/SYCL/Matrix/XMX8/get_coord_bf16_gemm.cpp b/SYCL/Matrix/XMX8/get_coord_bf16_gemm.cpp
new file mode 100644
index 0000000000..2890298389
--- /dev/null
+++ b/SYCL/Matrix/XMX8/get_coord_bf16_gemm.cpp
@@ -0,0 +1,26 @@
+//==----------- get_coord_bf16_gemm.cpp  - DPC++ joint_matrix---------==//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+// REQUIRES: matrix-xmx8
+
+// RUN: %clangxx -fsycl %s -o %t.out -DSYCL_EXT_ONEAPI_MATRIX_VERSION=4
+// RUN: %CPU_RUN_PLACEHOLDER %t.out
+// RUN: %GPU_RUN_PLACEHOLDER %t.out
+// XFAIL:*
+
+#include <iostream>
+#include <random>
+#include <sycl/sycl.hpp>
+
+using namespace sycl;
+using namespace sycl::ext::intel;
+using namespace sycl::ext::oneapi::experimental::matrix;
+using bfloat16 = sycl::ext::oneapi::bfloat16;
+
+#define SG_SZ 8
+
+#include "../get_coord_bf16_gemm_impl.hpp"
diff --git a/SYCL/Matrix/XMX8/get_coord_bf16_matA.cpp b/SYCL/Matrix/XMX8/get_coord_bf16_matA.cpp
new file mode 100644
index 0000000000..e80464c1ae
--- /dev/null
+++ b/SYCL/Matrix/XMX8/get_coord_bf16_matA.cpp
@@ -0,0 +1,26 @@
+//==----------- get_coord_bf16_matA.cpp  - DPC++ joint_matrix---------==//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+// REQUIRES: matrix-xmx8
+
+// RUN: %clangxx -fsycl %s -o %t.out -DSYCL_EXT_ONEAPI_MATRIX_VERSION=4
+// RUN: %CPU_RUN_PLACEHOLDER %t.out
+// RUN: %GPU_RUN_PLACEHOLDER %t.out
+// XFAIL:*
+
+#include <iostream>
+#include <random>
+#include <sycl/sycl.hpp>
+
+using namespace sycl;
+using namespace sycl::ext::intel;
+using namespace sycl::ext::oneapi::experimental::matrix;
+using bfloat16 = sycl::ext::oneapi::bfloat16;
+
+#define SG_SZ 8
+
+#include "../get_coord_bf16_matA_impl.hpp"
diff --git a/SYCL/Matrix/XMX8/get_coord_bf16_matB.cpp b/SYCL/Matrix/XMX8/get_coord_bf16_matB.cpp
new file mode 100644
index 0000000000..6c7bbf7da1
--- /dev/null
+++ b/SYCL/Matrix/XMX8/get_coord_bf16_matB.cpp
@@ -0,0 +1,26 @@
+//==----------- get_coord_bf16_matB.cpp  - DPC++ joint_matrix---------==//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+// REQUIRES: matrix-xmx8
+
+// RUN: %clangxx -fsycl %s -o %t.out -DSYCL_EXT_ONEAPI_MATRIX_VERSION=4
+// RUN: %CPU_RUN_PLACEHOLDER %t.out
+// RUN: %GPU_RUN_PLACEHOLDER %t.out
+// XFAIL:*
+
+#include <iostream>
+#include <random>
+#include <sycl/sycl.hpp>
+
+using namespace sycl;
+using namespace sycl::ext::intel;
+using namespace sycl::ext::oneapi::experimental::matrix;
+using bfloat16 = sycl::ext::oneapi::bfloat16;
+
+#define SG_SZ 8
+
+#include "../get_coord_bf16_matB_impl.hpp"
diff --git a/SYCL/Matrix/get_coord_bf16_gemm.cpp b/SYCL/Matrix/get_coord_bf16_gemm.cpp
new file mode 100644
index 0000000000..61fac9cc98
--- /dev/null
+++ b/SYCL/Matrix/get_coord_bf16_gemm.cpp
@@ -0,0 +1,27 @@
+//==----------- get_coord_bf16_gemm.cpp  - DPC++ joint_matrix---------==//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+// REQUIRES: matrix
+
+// RUN: %clangxx -fsycl %s -o %t.out -DSYCL_EXT_ONEAPI_MATRIX_VERSION=4
+// RUN: %CPU_RUN_PLACEHOLDER %t.out
+// RUN: %GPU_RUN_PLACEHOLDER %t.out
+// XFAIL:*
+
+#include <iostream>
+#include <random>
+#include <sycl/sycl.hpp>
+
+using namespace sycl;
+using namespace sycl::ext::intel;
+using namespace sycl::ext::oneapi;
+using namespace sycl::ext::oneapi::experimental::matrix;
+using bfloat16 = sycl::ext::oneapi::bfloat16;
+
+#define SG_SZ 16
+
+#include "get_coord_bf16_gemm_impl.hpp"
diff --git a/SYCL/Matrix/get_coord_bf16_gemm_impl.hpp b/SYCL/Matrix/get_coord_bf16_gemm_impl.hpp
new file mode 100644
index 0000000000..8b36f20c27
--- /dev/null
+++ b/SYCL/Matrix/get_coord_bf16_gemm_impl.hpp
@@ -0,0 +1,208 @@
+#define TM 8
+#define TN SG_SZ
+#define TK 16
+
+static constexpr size_t MATRIX_M = TM * 2;
+static constexpr size_t MATRIX_N = TN * 2;
+static constexpr size_t MATRIX_K = TK * 2;
+
+#define BF16_EPSILON 0.00781250
+
+template <typename T, size_t NUM_ROWS, size_t NUM_COLS> struct big_matrix {
+private:
+  T *mat;
+
+public:
+  T *get_data() { return mat; }
+  void set_data(T *data) { mat = data; }
+  big_matrix(T *data) : mat(data) {}
+};
+
+// clang-format off
+/*
+Here's how the data is distributed
+W0 --> 0 1 2 3 4 5 6 7
+wi [0,0] -> i=0, [0, 0]        wi [0,1] --> i=0, [0, 1]     wi [0,15] --> i=0, [0, 15]
+            i=1, [1, 0]                     i=1, [1, 1]                   i=1, [1, 15]
+            i=2, [2, 0]                     i=2, [2, 1]                   ...
+            ...                             ....
+            i=7, [7, 0]                     i=7, [7, 1]
+*/
+// clang-format on
+std::tuple<uint32_t, uint32_t> get_coord_ref(int i, int wi_number) {
+  return std::make_tuple(i, wi_number);
+}
+
+float sum_rows[MATRIX_M] = {0};
+
+template <typename T1, typename T2, size_t M, size_t N, size_t K>
+void matrix_multiply(big_matrix<T1, M, N> &C, big_matrix<T2, M, K> &A,
+                     big_matrix<T2, K / 2, N * 2> &B) {
+  size_t NDRangeM = M / TM;
+  size_t NDRangeN = N / TN;
+  buffer<bfloat16, 2> bufA(A.get_data(), range<2>(M, K));
+  buffer<bfloat16, 2> bufB(B.get_data(), range<2>(K, N));
+  buffer<float, 2> bufC((float *)C.get_data(), range<2>(M, N));
+
+  buffer<float> sum_rows_v(sum_rows, M); // there are total of M rows
+
+  queue q;
+  q.submit([&](handler &cgh) {
+     auto accC = bufC.get_access<access::mode::read_write>(cgh);
+     auto accA = bufA.get_access<access::mode::read_write>(cgh);
+     auto accB = bufB.get_access<access::mode::read_write>(cgh);
+
+     auto v = sum_rows_v.get_access<access::mode::read_write>(cgh);
+     auto os = sycl::stream(100000, 6144, cgh);
+
+     cgh.parallel_for<class imatrix>(
+         nd_range<2>({NDRangeM, NDRangeN * SG_SZ}, {1, 1 * SG_SZ}),
+         [=](nd_item<2> spmd_item) [[intel::reqd_sub_group_size(SG_SZ)]]
+
+         {
+           // The submatrix API has to be accessed by all the workitems in a
+           // subgroup these functions will be called once by the subgroup no
+           // code divergence between the workitems
+           const auto global_idx = spmd_item.get_global_id(0);
+           const auto global_idy = spmd_item.get_global_id(1);
+           const auto sg_startx = global_idx - spmd_item.get_local_id(0);
+           const auto sg_starty = global_idy - spmd_item.get_local_id(1);
+
+           sub_group sg = spmd_item.get_sub_group();
+           joint_matrix<sub_group, bfloat16, use::a, TM, TK, layout::row_major>
+               sub_a;
+           // For B, we assume B has been already VNNIed.
+           joint_matrix<sub_group, bfloat16, use::b, TK, TN,
+                        ext::intel::experimental::matrix::layout::packed>
+               sub_b;
+           joint_matrix<sub_group, float, use::accumulator, TM, TN> sub_c;
+
+           joint_matrix_load(sg, sub_c,
+                             accC.get_pointer() + (sg_startx * TM) * N +
+                                 sg_starty / SG_SZ * TN,
+                             N, layout::row_major);
+           for (int k = 0; k < K / TK; k += 1) { //
+             joint_matrix_load(
+                 sg, sub_a, accA.get_pointer() + (sg_startx * TM) * K + k * TK,
+                 K);
+             joint_matrix_load(sg, sub_b,
+                               accB.get_pointer() + (k * TK / 2) * (N * 2) +
+                                   sg_starty / SG_SZ * TN * 2,
+                               N * 2);
+             sub_c = joint_matrix_mad(sg, sub_a, sub_b, sub_c);
+           }
+           joint_matrix_store(sg, sub_c,
+                              accC.get_pointer() + (sg_startx * TM) * N +
+                                  sg_starty / SG_SZ * TN,
+                              N, layout::row_major);
+
+           float sum_local_rows[M] = {0}; // 8 local rows, M total
+           auto data =
+               sycl::ext::intel::experimental::matrix::get_wi_data(sg, sub_c);
+
+           // Keep track of rows handled in this WI
+           int32_t handled_rows[M] = {-1};
+           size_t
+               global_index; // Index into the result array that holds the sums.
+
+           for (int i = 0; i < data.length(); ++i) {
+             auto dataItem = data[i];
+             auto [row, col] = dataItem.get_coord();
+             // get_coord_ref(i, spmd_item.get_local_id(1));
+             global_index = row + global_idx * TM;
+
+             sum_local_rows[global_index] += data[i];
+
+             handled_rows[global_index] = 1;
+           }
+
+           for (int j = 0; j < M; j++) {
+             if (handled_rows[j] == 1) {
+               global_index = j;
+               sum_local_rows[global_index] = reduce_over_group(
+                   sg, sum_local_rows[global_index], sycl::plus<>());
+               // only Groups leader perform the global reduction
+               if (global_idy % SG_SZ == 0) {
+                 sycl::atomic_ref<float, sycl::memory_order::relaxed,
+                                  sycl::memory_scope::device>
+                     aref(v[global_index]);
+                 aref.fetch_add(sum_local_rows[global_index]);
+               }
+             }
+           }
+         }); // parallel for
+   }).wait();
+}
+
+bfloat16 A[MATRIX_M][MATRIX_K];
+bfloat16 B[MATRIX_K / 2][MATRIX_N * 2];
+float C[MATRIX_M][MATRIX_N];
+float D[MATRIX_M][MATRIX_N];
+
+float make_fp32(bfloat16 x) {
+  unsigned int y = *((int *)&x);
+  y = y << 16;
+  float *res = reinterpret_cast<float *>(&y);
+  return *res;
+}
+
+void matrix_multiply_ref(int *A_mem, int *B_mem, int *C_mem, int M, int N,
+                         int K) {
+  for (int m = 0; m < M; m++)
+    for (int n = 0; n < N; n++) {
+      for (int k = 0; k < K; k++) {
+        // Because B was assumed VNNIed
+        bfloat16 *va = (bfloat16 *)(A_mem + m * K + k);
+        bfloat16 *vb = (bfloat16 *)(B_mem + k * N + n);
+        float acc = *((float *)(C_mem + m * N + n));
+        for (int i = 0; i < 2; i++) {
+          acc += (make_fp32(va[i]) * make_fp32(vb[i]));
+        }
+        *((float *)(C_mem + m * N + n)) = acc;
+      }
+    }
+}
+
+int main() {
+  for (int i = 0; i < MATRIX_M; i++) {
+    for (int j = 0; j < MATRIX_K; j++) {
+      A[i][j] = bfloat16(1.0f * (i + j));
+    }
+  }
+  for (int i = 0; i < MATRIX_K / 2; i++) {
+    for (int j = 0; j < MATRIX_N * 2; j++) {
+      B[i][j] = bfloat16(2.0f * i + 3.0f * j);
+    }
+  }
+  for (int i = 0; i < MATRIX_M; i++) {
+    for (int j = 0; j < MATRIX_N; j++) {
+      C[i][j] = 1.0;
+      D[i][j] = 1.0;
+    }
+  }
+
+  big_matrix<float, MATRIX_M, MATRIX_N> MC((float *)&C);
+  big_matrix<float, MATRIX_M, MATRIX_N> MD((float *)&D);
+  big_matrix<bfloat16, MATRIX_M, MATRIX_K> MA((bfloat16 *)&A);
+  big_matrix<bfloat16, MATRIX_K / 2, MATRIX_N * 2> MB((bfloat16 *)&B);
+  matrix_multiply(MC, MA, MB);
+  matrix_multiply_ref((int32_t *)A, (int32_t *)B, (int32_t *)D, MATRIX_M,
+                      MATRIX_N, MATRIX_K / 2);
+
+  bool res = true;
+  float sum_rows_ref[MATRIX_M] = {0};
+
+  for (int i = 0; i < MATRIX_M; i++) {
+    for (int j = 0; j < MATRIX_N; j++) {
+      // std::cout << C[i][j] << " ";
+      if ((fabs(C[i][j]) - fabs(D[i][j])) > BF16_EPSILON)
+        res = false;
+      sum_rows_ref[i] += C[i][j];
+    }
+    if ((fabs(sum_rows_ref[i]) - fabs(sum_rows[i])) > BF16_EPSILON)
+      res = false;
+    // std::cout << "\n";
+  }
+  std::cout << (res ? "passed" : "failed") << std::endl;
+  return !res;
+}
diff --git a/SYCL/Matrix/get_coord_bf16_matA.cpp b/SYCL/Matrix/get_coord_bf16_matA.cpp
new file mode 100644
index 0000000000..e5570c9abb
--- /dev/null
+++ b/SYCL/Matrix/get_coord_bf16_matA.cpp
@@ -0,0 +1,27 @@
+//==----------- get_coord_bf16_matA.cpp  - DPC++ joint_matrix---------==//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+// REQUIRES: matrix
+
+// RUN: %clangxx -fsycl %s -o %t.out -DSYCL_EXT_ONEAPI_MATRIX_VERSION=4
+// RUN: %CPU_RUN_PLACEHOLDER %t.out
+// RUN: %GPU_RUN_PLACEHOLDER %t.out
+// XFAIL:*
+
+#include <iostream>
+#include <random>
+#include <sycl/sycl.hpp>
+
+using namespace sycl;
+using namespace sycl::ext::intel;
+using namespace sycl::ext::oneapi;
+using namespace sycl::ext::oneapi::experimental::matrix;
+using bfloat16 = sycl::ext::oneapi::bfloat16;
+
+#define SG_SZ 16
+
+#include "get_coord_bf16_matA_impl.hpp"
diff --git a/SYCL/Matrix/get_coord_bf16_matA_impl.hpp b/SYCL/Matrix/get_coord_bf16_matA_impl.hpp
new file mode 100644
index 0000000000..3d10598fa2
--- /dev/null
+++ b/SYCL/Matrix/get_coord_bf16_matA_impl.hpp
@@ -0,0 +1,187 @@
+#define TM 8
+#define TN SG_SZ
+#define TK 32
+
+template <typename T, size_t NUM_ROWS, size_t NUM_COLS> struct big_matrix {
+public:
+  T *mat;
+
+public:
+  T *get_data() { return mat; }
+  void set_data(T *data) { mat = data; }
+  big_matrix(T *data) : mat(data) {}
+};
+
+template <typename T, size_t M, size_t K>
+void sum_rows_ref(host_accessor<T, 2, access::mode::read_write> A,
+                  host_accessor<int, 1, access::mode::read_write> sum_rows) {
+  int sum_rows_ref[M] = {0};
+  for (size_t i = 0; i < M; i++) {
+    for (size_t j = 0; j < K; j++) {
+      sum_rows_ref[i] += A[i][j];
+    }
+    auto diff = sum_rows[i] - sum_rows_ref[i];
+    assert(std::fabs(static_cast<int>(diff)) <=
+           std::numeric_limits<int>::epsilon());
+  }
+}
+
+// clang-format off
+/*
+Here's how the data is distributed among work items
+0 0  0 0
+/
+/
+1 1  1 1
+/
+/
+2 2  2 2
+/
+/ 
+3 3  3 3 
+W0 --> 0 0 1 1 2 2 3 3 .... 7 7
+wi [0,0] -> i=0, [0, 0]        wi [0,1] --> i=0, [0, 2]     wi [0,15] --> i=0, [0, 30]
+            i=1, [0, 1]                     i=1, [0, 3]                   i=1, [0, 31]
+            i=2, [1, 0]                     i=2, [1, 2]                   i=2, [1, 30]
+            i=3, [1, 1]                     i=3, [1, 3]                   i=3, [1, 31]
+            i=4, [2, 0]                     i=4, [2, 2]                   ...
+            i=5, [2, 1]                     i=5, [2, 3]
+            ...                             ....
+            i=14,[7, 0]                     i=14, [7, 2]
+            i=15,[7, 1]                     i=15, [7, 3]                  i=15, [7, 31]
+*/
+//clang-format on
+std::tuple<uint32_t, uint32_t> get_coord_ref(int i, int wi_number) {
+  return std::make_tuple(i/2, ((i%2) + (wi_number*2)));
+}
+
+//clang-format off
+/* 
+Here's how the distribution of the A matrix looks like for this test case
+x x x x  x x x x    x x x x  x x x x  x x x x  x x x x    x x x x  x x x x
+x x x x  x x x x    x x x x  x x x x  x x x x  x x x x    x x x x  x x x x
+x x x x  x x x x    x x x x  x x x x  x x x x  x x x x    x x x x  x x x x
+x x x x  x x x x    x x x x  x x x x  x x x x  x x x x    x x x x  x x x x
+x x x x  x x x x    x x x x  x x x x  x x x x  x x x x    x x x x  x x x x
+x x x x  x x x x    x x x x  x x x x  x x x x  x x x x    x x x x  x x x x
+x x x x  x x x x    x x x x  x x x x  x x x x  x x x x    x x x x  x x x x
+x x x x  x x x x    x x x x  x x x x  x x x x  x x x x    x x x x  x x x x
+<---------------------------------  SG1 --------------------------------->
+x x x x  x x x x    x x x x  x x x x  x x x x  x x x x    x x x x  x x x x
+x x x x  x x x x    x x x x  x x x x  x x x x  x x x x    x x x x  x x x x
+x x x x  x x x x    x x x x  x x x x  x x x x  x x x x    x x x x  x x x x
+x x x x  x x x x    x x x x  x x x x  x x x x  x x x x    x x x x  x x x x
+x x x x  x x x x    x x x x  x x x x  x x x x  x x x x    x x x x  x x x x
+x x x x  x x x x    x x x x  x x x x  x x x x  x x x x    x x x x  x x x x
+x x x x  x x x x    x x x x  x x x x  x x x x  x x x x    x x x x  x x x x
+x x x x  x x x x    x x x x  x x x x  x x x x  x x x x    x x x x  x x x x
+<0> <1>  <2> <3>    <4> <5>  <6> <7>  ..... WORK ITEMS
+Each work item has 16 elements <8 rows and 2 cols of the original matrix>
+the data_slice in holds the matrix elements in the following order:
+0 0  0 0
+   /
+  /
+1 1  1 1
+   /
+  /
+2 2  2 2
+  /
+ / 
+3 3  3 3 
+W0 --> 0 0 1 1 2 2 3 3 .... 7 7
+*/
+//clang-format on
+template <typename T, size_t M, size_t K>
+void matrix_sum_rows(queue q, big_matrix<T, M, K> &A, nd_range<2> &r) {
+  buffer<int8_t, 2> bufA(A.get_data(), range<2>(M, K));
+  // size of vector is known because SG size of set by the user in this case
+  int sum_rows[M] = {0};
+  buffer<int> sum_rows_v(sum_rows, M); // there are total of M rows
+  q.submit([&](handler &cgh) {
+     auto accA = bufA.get_access<access::mode::read_write>(cgh);
+
+     auto v = sum_rows_v.get_access<access::mode::atomic>(cgh);
+     auto os = sycl::stream(100000, 6144, cgh);
+
+     cgh.parallel_for<class add_matrix>(
+         r, [=](nd_item<2> spmd_item) [[intel::reqd_sub_group_size(SG_SZ)]] {
+           const auto global_idx = spmd_item.get_global_id(0);
+           const auto global_idy = spmd_item.get_global_id(1);
+           const auto sg_startx = global_idx - spmd_item.get_local_id(0);
+           const auto sg_starty = global_idy - spmd_item.get_local_id(1);
+
+           ext::oneapi::sub_group sg = spmd_item.get_sub_group();
+
+           // TM = 8, TK = 32
+          joint_matrix<sub_group, int8_t, use::a, TM, TK, layout::row_major>
+               sub_a;
+
+          joint_matrix_load(
+                 sg, sub_a, accA.get_pointer() + (global_idx * TM * K) + TK,
+                 K); 
+
+           // calculate sum of rows in sum_rows_v[8], there are 8 rows in sub_a
+           int32_t sum_local_rows[M] = {0}; // 8 local rows, M total
+           // sub_a has 8x32 elements, 16 elements per WI, 2 per WI per row
+          auto data = sycl::ext::intel::experimental::matrix::get_wi_data(sg, sub_a);
+
+           size_t global_index; // Index into the result array that holds the sums.
+          
+          // Keep track of rows handled in this WI
+          int32_t handled_rows[M] = {-1};
+
+          //  each WI calculates local sum of rows
+          for (int i = 0; i < data.length(); ++i) {
+            // get the index of the element in the submatrix
+            auto data_item = data[i];
+            auto [row, col] = data_item.get_coord();
+            global_index = row + global_idx*TM;
+
+            sum_local_rows[global_index] += data[i];
+
+            handled_rows[global_index] = 1;
+          }
+          
+          for (int j=0; j < M; j++) {
+              if (handled_rows[j] == 1) {
+                global_index = j;
+                sum_local_rows[global_index] = reduce_over_group(
+                    sg, sum_local_rows[global_index],
+                    sycl::plus<>());
+                // only Groups leader perform the global reduction
+                if (global_idy % SG_SZ == 0) {
+                  atomic_fetch_add(v[global_index],
+                                  sum_local_rows[global_index]);
+                }
+              }
+          } 
+         }); // parallel for
+   }).wait();
+  sum_rows_ref<T, M, K>(bufA.get_host_access(), sum_rows_v.get_host_access());
+}
+
+
+static constexpr size_t MATRIX_M = TM * 2;
+static constexpr size_t MATRIX_K = TK * 2;
+int8_t A[MATRIX_M][MATRIX_K];
+
+int main() {
+  big_matrix<int8_t, MATRIX_M, MATRIX_K> MA((int8_t *)&A);
+
+  size_t NDRangeM = MATRIX_M / TM;
+  size_t NDRangeK = MATRIX_K / TK;
+  queue q;
+  nd_range<2> r({NDRangeM, NDRangeK * SG_SZ}, {1, 1 * SG_SZ});
+
+  for (int i = 0; i < MATRIX_M; i++) {
+    for (int j = 0; j < MATRIX_K; j++) {
+      A[i][j] = i;
+    }
+  }
+
+  matrix_sum_rows<int8_t, MATRIX_M, MATRIX_K>(q, MA, r);
+
+  std::cout << "Passed\n";
+
+  return 0;
+}
diff --git a/SYCL/Matrix/get_coord_bf16_matB.cpp b/SYCL/Matrix/get_coord_bf16_matB.cpp
new file mode 100644
index 0000000000..d3f89079ce
--- /dev/null
+++ b/SYCL/Matrix/get_coord_bf16_matB.cpp
@@ -0,0 +1,27 @@
+//==----------- get_coord_bf16_matB.cpp  - DPC++ joint_matrix---------==//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+// REQUIRES: matrix
+
+// RUN: %clangxx -fsycl %s -o %t.out -DSYCL_EXT_ONEAPI_MATRIX_VERSION=4
+// RUN: %CPU_RUN_PLACEHOLDER %t.out
+// RUN: %GPU_RUN_PLACEHOLDER %t.out
+// XFAIL:*
+
+#include <iostream>
+#include <random>
+#include <sycl/sycl.hpp>
+
+using namespace sycl;
+using namespace sycl::ext::intel;
+using namespace sycl::ext::oneapi;
+using namespace sycl::ext::oneapi::experimental::matrix;
+using bfloat16 = sycl::ext::oneapi::bfloat16;
+
+#define SG_SZ 16
+
+#include "get_coord_bf16_matB_impl.hpp"
diff --git a/SYCL/Matrix/get_coord_bf16_matB_impl.hpp b/SYCL/Matrix/get_coord_bf16_matB_impl.hpp
new file mode 100644
index 0000000000..8326b986b1
--- /dev/null
+++ b/SYCL/Matrix/get_coord_bf16_matB_impl.hpp
@@ -0,0 +1,218 @@
+#define TN SG_SZ
+#define TK 32
+
+template <typename T, size_t NUM_ROWS, size_t NUM_COLS> struct big_matrix {
+public:
+  T *mat;
+
+public:
+  T *get_data() { return mat; }
+  void set_data(T *data) { mat = data; }
+  big_matrix(T *data) : mat(data) {}
+};
+
+template <typename T, size_t M, size_t N>
+void sum_cols_ref(host_accessor<T, 2, access::mode::read_write> B,
+                  host_accessor<int, 1, access::mode::read_write> sum_cols) {
+  int sum_cols_ref[N] = {0};
+  for (size_t j = 0; j < N; j++) {
+    for (size_t i = 0; i < M; i++) {
+      sum_cols_ref[j] += B[i][j];
+    }
+    auto diff = sum_cols[j] - sum_cols_ref[j];
+    assert(std::fabs(static_cast<int>(diff)) <=
+           std::numeric_limits<int>::epsilon());
+  }
+}
+
+// clang-format off
+/* 
+    Here is a demonstration of how matrix B will be divided across
+    work items for this test case.
+    <    ---------------    128    ---------------------------------->
+    x x x x x x x x x x x x x x x x       ..........    x x x x x x   ^
+    x x x x x x x x x x x x x x x x       ..........    x x x x x x  16
+    x x x x x x x x x x x x x x x x       ..........    x x x x x x   |
+    .....                                                             |
+    x x x x x x x x x x x x x x x x       ..........    x x x x x x   |
+    x x x x x x x x x x x x x x x x       ..........    x x x x x x   v
+    
+    ---------------    64    ---------------->
+    x x x x   x x    ..........    x x  x x x x   ^
+    x x x x   x x    ..........    x x  x x x x   8
+    x x x x   x x    ..........    x x  x x x x   |  <-- part of (VNNI-ed) 
+    .....                                         |   original matrix each SG
+    x x x x   x x    ..........    x x  x x x x   |   holds
+    x x x x   x x    ..........    x x  x x x x   v
+    < WI0 >                            < WI15 >
+    <--------    16    ------------->
+    x x x     ..........    x x x   ^
+    x x x     ..........    x x x   |
+    x x x     ..........    x x x   | <-- part of (non-VNNI-ed) original matrix
+    .....                           |           each SG holds
+    x x x     ..........    x x x   |
+    x x x     ..........    x x x   |
+    x x x     ..........    x x x  32
+    x x x     ..........    x x x   |
+    x x x     ..........    x x x   |
+    x x x     ..........    x x x   |
+    x x x     ..........    x x x   |
+    x x x     ..........    x x x   |
+    x x x     ..........    x x x   v
+    If we dividie the above matrix across 16 (SG_SZ) work items,
+    each WI will hold 32 elements.  And these 32 elements will be
+    8x4 chunks as shown in the VNNI-ed matrix figure. 
+*/
+
+// The total distribution among the WIs in ALL the sub-groups is as follows:
+// This is useful to figure out the the global index is to be calculated
+
+/*
+W0 --> 0 0 0 0   1 1 1 1 ...   7 7 7 7 --> total 32 elements
+wi [0,0] --> i=0, [0, 0]        wi [0,1] --> i=0, [0, 4]     wi [0,15] --> i=0, [0, 60] | wi [0,16] --> i=0, [0, 64]
+            i=1, [0, 1]                     i=1, [0, 5]                   i=1, [0, 61]  |               i=1, [0, 65]
+            i=2, [0, 2]                     i=2, [0, 6]                   i=2, [0, 62]  |               i=2, [0, 66]
+            i=3, [0, 3]                     i=3, [0, 7]                   i=3, [0, 63]  |               i=3, [0, 67]              
+            i=4, [1, 0]                     i=4, [1, 4]                   i=4, [1, 60]  |               ....
+            i=5, [1, 1]                     i=5, [1, 5]                   i=5, [1, 61]  |
+            i=6, [1, 2]                     i=6, [1, 6]                   i=6, [1, 62]  |
+            i=7, [1, 3]                     i=7, [1, 7]                   i=7, [1, 63]  |
+            ...                             ...                           ....          |
+            i=28,[7, 0]                     i=28,[7, 4]                   i=28,[7, 60]  |               i=28, [7, 124]
+            i=29,[7, 1]                     i=29,[7, 5]                   i=29,[7, 61]  |               i=29, [7, 125]
+            i=30,[7, 2]                     i=30,[7, 6]                   i=30,[7, 62]  |               i=30, [7, 126]
+            i=31,[7, 3]                     i=31,[7, 7]                   i=31,[7, 63]  |               i=31, [7, 127]
+---------------------------------------------------------------------------------------- ---------------------------
+wi [1,0] -->    i=0, [8, 0]
+                i=1, [8, 1]
+                i=2, [8, 2]
+                i=3, [8, 2]
+                ...
+                i=28, [15, 0]
+                i=29, [15, 1]
+                i=30, [15, 2]
+                i=31, [15, 3]
+*/
+
+// The following is the distribution among WIs in a SINGLE SG.
+/*
+W0 --> 0 0 0 0   1 1 1 1 ...   7 7 7 7 --> total 32 elements
+wi [0,0] -> i=0, [0, 0]        wi [0,1] --> i=0, [0, 4]     wi [0,15] --> i=0, [0, 60]  | 
+            i=1, [0, 1]                     i=1, [0, 5]                   i=1, [0, 61]  |               
+            i=2, [0, 2]                     i=2, [0, 6]                   i=2, [0, 62]  |               
+            i=3, [0, 3]                     i=3, [0, 7]                   i=3, [0, 63]  |                              
+            i=4, [1, 0]                     i=4, [1, 4]                   i=4, [1, 60]  |
+            i=5, [1, 1]                     i=5, [1, 5]                   i=5, [1, 61]  |
+            i=6, [1, 2]                     i=6, [1, 6]                   i=6, [1, 62]  |
+            i=7, [1, 3]                     i=7, [1, 7]                   i=7, [1, 63]  |
+            ...                             ...                           ....          |
+            i=28,[7, 0]                     i=28,[7, 4]                   i=28,[7, 60]  |
+            i=29,[7, 1]                     i=29,[7, 5]                   i=29,[7, 61]  |
+            i=30,[7, 2]                     i=30,[7, 6]                   i=30,[7, 62]  |
+            i=31,[7, 3]                     i=31,[7, 7]                   i=31,[7, 63]  |
+*/
+// clang-format on
+
+template <typename T, size_t M, size_t N>
+void matrix_sum_cols(queue q, big_matrix<T, M, N> &B, nd_range<2> &r) {
+  buffer<int8_t, 2> bufB(B.get_data(), range<2>(M, N));
+  // size of vector is known because SG size of set by the user in this case
+  int sum_cols[N] = {0};
+  buffer<int> sum_cols_v(sum_cols, N); // there are total of tK/4 * 2, 16 rows
+  q.submit([&](handler &cgh) {
+     auto accB = bufB.get_access<access::mode::read_write>(cgh);
+
+     auto v = sum_cols_v.get_access<access::mode::atomic>(cgh);
+     auto os = sycl::stream(100000, 6144, cgh);
+
+     cgh.parallel_for<class add_matrix>(
+         r, [=](nd_item<2> spmd_item) [[intel::reqd_sub_group_size(SG_SZ)]] {
+           const auto global_idx = spmd_item.get_global_id(0);
+           const auto global_idy = spmd_item.get_global_id(1);
+           const auto sg_startx = global_idx - spmd_item.get_local_id(0);
+           const auto sg_starty = global_idy - spmd_item.get_local_id(1);
+
+           ext::oneapi::sub_group sg = spmd_item.get_sub_group();
+
+           // TK = 32, TN = 16
+           joint_matrix<sub_group, int8_t, use::b, TK, TN,
+                        ext::intel::experimental::matrix::layout::packed>
+               sub_b;
+
+           joint_matrix_load(sg, sub_b,
+                             accB.get_pointer() + (global_idx * (TK / 4) * N) +
+                                 sg_starty / SG_SZ * TN * 4,
+                             N);
+
+           int32_t sum_local_cols[N] = {0}; // 4 local cols, N total
+           // sub_b has 32x16 elements, 32 elements per WI, 4 per WI per row
+           auto wiData =
+               sycl::ext::intel::experimental::matrix::get_wi_data(sg, sub_b);
+
+           size_t
+               global_index; // Index into the result array that holds the sums.
+
+           // Keep track of cols handled in this WI
+           int32_t handled_cols[N] = {-1};
+
+           //  each WI calculates local sum of cols
+           for (int i = 0; i < wiData.length(); ++i) {
+             // get the index of the element in the submatrix
+             auto dataItem = wiData[i];
+             auto [row, col] = dataItem.get_coord();
+
+             // Calculation of global index
+             int sg_idx = (int)global_idy / SG_SZ;
+             global_index = col + sg_idx * 4 /*VNNI_FACTOR*/ * SG_SZ;
+             sum_local_cols[global_index] += wiData[i];
+             handled_cols[global_index] = 1;
+           }
+
+           for (int j = 0; j < N; j++) {
+             if (handled_cols[j] == 1) {
+               global_index = j;
+               sum_local_cols[global_index] = reduce_over_group(
+                   sg, sum_local_cols[global_index], sycl::plus<>());
+               // TODO: Do we need a reduce_over_grp? Adding it does not
+               // make any difference in result
+               atomic_fetch_add(v[global_index], sum_local_cols[global_index]);
+             }
+           }
+         }); // parallel for
+   }).wait();
+  sum_cols_ref<T, M, N>(bufB.get_host_access(), sum_cols_v.get_host_access());
+}
+
+// TK = 32, TN = 16
+static constexpr size_t MATRIX_K = TK / 4 * 2; // 16
+static constexpr size_t MATRIX_N = TN * 4 * 2; // 128
+int8_t B[MATRIX_K][MATRIX_N];
+
+/* <    ---------------    128    ---------------------------------->
+   x x x x x x x x x x x x x x x x       ..........    x x x x x x   ^
+   x x x x x x x x x x x x x x x x       ..........    x x x x x x  16
+   x x x x x x x x x x x x x x x x       ..........    x x x x x x   |
+   .....                                                             |
+   x x x x x x x x x x x x x x x x       ..........    x x x x x x   |
+   x x x x x x x x x x x x x x x x       ..........    x x x x x x   v
+*/
+int main() {
+  big_matrix<int8_t, MATRIX_K, MATRIX_N> MB((int8_t *)&B);
+
+  size_t NDRangeK = MATRIX_K / (TK / 4);
+  size_t NDRangeN = (MATRIX_N / 4) / TN;
+  queue q;
+  nd_range<2> r({NDRangeK, NDRangeN * SG_SZ}, {1, 1 * SG_SZ});
+
+  for (int i = 0; i < MATRIX_K; i++) {
+    for (int j = 0; j < MATRIX_N; j++) {
+      B[i][j] = i;
+    }
+  }
+
+  matrix_sum_cols<int8_t, MATRIX_K, MATRIX_N>(q, MB, r);
+
+  std::cout << "Passed\n";
+
+  return 0;
+}