Added gaussian implementation

Author: roxx30198

dpbench/benchmarks/rodinia/CMakeLists.txt

@@ -1,3 +1,5 @@
 # SPDX-FileCopyrightText: 2022 - 2023 Intel Corporation
 #
 # SPDX-License-Identifier: Apache-2.0
+
+add_subdirectory(gaussian)

dpbench/configs/bench_info/rodinia/sample.toml renamed to dpbench/benchmarks/rodinia/gaussian/CMakeLists.txt

@@ -1,3 +1,5 @@
 # SPDX-FileCopyrightText: 2022 - 2023 Intel Corporation
 #
 # SPDX-License-Identifier: Apache-2.0
+
+add_subdirectory(gaussian_sycl_native_ext)

dpbench/benchmarks/rodinia/gaussian/__init__.py

@@ -0,0 +1,19 @@
+# SPDX-FileCopyrightText: 2022 - 2023 Intel Corporation
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""Gaussian elimination implementation."""
+
+"""This is sycl and numba-dpex implementation for gaussian elimination
+Input
+---------
+size<int_64> : Forms an input matrix of dimensions (size x size)
+Output
+--------
+result<array<float>> : Result of the given set of linear equations using
+                        gaussian elimination.
+Method:
+The gaussian transformations are applied to the input matrix to form the
+diagonal matrix in forward elimination, and then the equations are solved
+to find the result in back substitution.
+"""

dpbench/benchmarks/rodinia/gaussian/gaussian_initialize.py

@@ -0,0 +1,45 @@
+# SPDX-FileCopyrightText: 2022 - 2023 Intel Corporation
+#
+# SPDX-License-Identifier: Apache-2.0
+"""Initialization function for matrices for gaussian elimination."""
+
+
+def initialize(size, Lambda, types_dict=None):
+    """Initialize the matrices based on size and type.
+
+    Args:
+        size: size for matrices(sizexsize).
+        Lambda: lambda value.
+        types_dict: data type of operand.
+
+    Returns: a: actual matrix.
+             b: base matrix (column matrix).
+             m: multiplier matrix.
+             result: result of operation.
+    """
+    import math
+
+    import numpy as np
+
+    dtype = types_dict["float"]
+
+    coe = np.empty((2 * size - 1), dtype=dtype)
+    a = np.empty((size * size), dtype=dtype)
+
+    for i in range(size):
+        coe_i = 10 * math.exp(Lambda * i)
+        j = size - 1 + i
+        coe[j] = coe_i
+        j = size - 1 - i
+        coe[j] = coe_i
+
+    for i in range(size):
+        for j in range(size):
+            a[i * size + j] = coe[size - 1 - i + j]
+
+    return (
+        a,
+        np.ones(size, dtype=dtype),
+        np.zeros((size * size), dtype=dtype),
+        np.zeros(size, dtype=dtype),
+    )

dpbench/benchmarks/rodinia/gaussian/gaussian_numba_dpex_k.py

@@ -0,0 +1,135 @@
+# SPDX-FileCopyrightText: 2022 - 2023 Intel Corporation
+#
+# SPDX-License-Identifier: Apache-2.0
+"""Numba-dpex implementation for gaussian elimination."""
+
+import dpctl
+import numba_dpex
+
+
+@numba_dpex.kernel()
+def gaussian_kernel_1(m, a, size, t):
+    """Find the multiplier matrix.
+
+    Args:
+        m: multiplier matrix.
+        a: input matrix.
+        size: sizew of matrix.
+        t: current iteration.
+    """
+    if (
+        numba_dpex.get_local_id(2)
+        + numba_dpex.get_group_id(2) * numba_dpex.get_local_size(2)
+        >= size - 1 - t
+    ):
+        return
+
+    m[
+        size
+        * (
+            numba_dpex.get_local_size(2) * numba_dpex.get_group_id(2)
+            + numba_dpex.get_local_id(2)
+            + t
+            + 1
+        )
+        + t
+    ] = (
+        a[
+            size
+            * (
+                numba_dpex.get_local_size(2) * numba_dpex.get_group_id(2)
+                + numba_dpex.get_local_id(2)
+                + t
+                + 1
+            )
+            + t
+        ]
+        / a[size * t + t]
+    )
+
+
+@numba_dpex.kernel()
+def gaussian_kernel_2(m, a, b, size, t):
+    """Perform Gaussian elimination using gaussian operations for a iteration.
+
+    Args:
+        m: multiplier matrix.
+        a: input matrix.
+        b: column matrix.
+        size: size of matrices.
+        t: current iteration.
+    """
+    if (
+        numba_dpex.get_local_id(2)
+        + numba_dpex.get_group_id(2) * numba_dpex.get_local_size(2)
+        >= size - 1 - t
+    ):
+        return
+
+    if (
+        numba_dpex.get_local_id(1)
+        + numba_dpex.get_group_id(1) * numba_dpex.get_local_size(1)
+        >= size - t
+    ):
+        return
+
+    xidx = numba_dpex.get_group_id(2) * numba_dpex.get_local_size(
+        2
+    ) + numba_dpex.get_local_id(2)
+    yidx = numba_dpex.get_group_id(1) * numba_dpex.get_local_size(
+        1
+    ) + numba_dpex.get_local_id(1)
+
+    a[size * (xidx + 1 + t) + (yidx + t)] -= (
+        m[size * (xidx + 1 + t) + t] * a[size * t + (yidx + t)]
+    )
+    if yidx == 0:
+        b[xidx + 1 + t] -= m[size * (xidx + 1 + t) + (yidx + t)] * b[t]
+
+
+def gaussian(a, b, m, size, block_sizeXY, result):
+    """Perform Gaussian elimination using gaussian operations.
+
+    Args:
+        a: input matrix.
+        b: column matrix.
+        m: multiplier matrix.
+        size: size of matrices.
+        block_sizeXY: grid size.
+        result: result matrix.
+    """
+    device = dpctl.SyclDevice()
+    block_size = device.max_work_group_size
+    grid_size = int((size / block_size) + 0 if not (size % block_size) else 1)
+
+    blocksize2d = block_sizeXY
+    gridsize2d = int(
+        (size / blocksize2d) + (0 if not (size % blocksize2d) else 1)
+    )
+
+    global_range = numba_dpex.Range(1, 1, grid_size * block_size)
+    local_range = numba_dpex.Range(1, 1, block_size)
+
+    dim_blockXY = numba_dpex.Range(1, blocksize2d, blocksize2d)
+    dim_gridXY = numba_dpex.Range(
+        1, gridsize2d * blocksize2d, gridsize2d * blocksize2d
+    )
+
+    for t in range(size - 1):
+        gaussian_kernel_1[numba_dpex.NdRange(global_range, local_range)](
+            m, a, size, t
+        )
+
+        gaussian_kernel_2[numba_dpex.NdRange(dim_gridXY, dim_blockXY)](
+            m, a, b, size, t
+        )
+
+    for i in range(size):
+        result[size - i - 1] = b[size - i - 1]
+        for j in range(i):
+            result[size - i - 1] -= (
+                a[size * (size - i - 1) + (size - j - 1)] * result[size - j - 1]
+            )
+        result[size - i - 1] = (
+            result[size - i - 1] / a[size * (size - i - 1) + (size - i - 1)]
+        )

dpbench/benchmarks/rodinia/gaussian/gaussian_python.py

@@ -0,0 +1,35 @@
+# SPDX-FileCopyrightText: 2022 - 2023 Intel Corporation
+#
+# SPDX-License-Identifier: Apache-2.0
+"""Gaussian elimination python serial implementation."""
+
+
+def gaussian(a, b, m, size, block_sizeXY, result):
+    """Python serial implementation for gaussian elimination.
+
+    Args:
+         a: actual matrix.
+         b: base matrix (column matrix).
+         m: multiplier matrix.
+         size: size for matrices(sizexsize).
+         block_sizeXY: block size for parallel 2d-kernel.
+         result: result of operation.
+    """
+    # Forward Elimination
+    for t in range(size - 1):
+        for i in range(t + 1, size):
+            m = a[i * size + t] / a[t * size + t]
+            for j in range(t, size):
+                a[i * size + j] = a[i * size + j] - m * a[t * size + j]
+            b[i] = b[i] - m * b[t]
+
+    # Back Substitution
+    for i in range(size):
+        result[size - i - 1] = b[size - i - 1]
+        for j in range(i):
+            result[size - i - 1] -= (
+                a[size * (size - i - 1) + (size - j - 1)] * result[size - j - 1]
+            )
+        result[size - i - 1] = (
+            result[size - i - 1] / a[size * (size - i - 1) + (size - i - 1)]
+        )

dpbench/benchmarks/rodinia/gaussian/gaussian_sycl_native_ext/CMakeLists.txt

@@ -0,0 +1,14 @@
+# SPDX-FileCopyrightText: 2022 - 2023 Intel Corporation
+#
+# SPDX-License-Identifier: Apache-2.0
+
+set(module_name gaussian_sycl)
+set(py_module_name _${module_name})
+python_add_library(${py_module_name} MODULE ${module_name}/${py_module_name}.cpp)
+add_sycl_to_target(TARGET ${py_module_name} SOURCES ${module_name}/${py_module_name}.cpp)
+target_include_directories(${py_module_name} PRIVATE ${Dpctl_INCLUDE_DIRS})
+
+file(RELATIVE_PATH py_module_dest ${CMAKE_SOURCE_DIR} ${CMAKE_CURRENT_SOURCE_DIR})
+install(TARGETS ${py_module_name}
+  DESTINATION ${py_module_dest}/${module_name}
+)

dpbench/benchmarks/rodinia/gaussian/gaussian_sycl_native_ext/__init__.py

@@ -0,0 +1,8 @@
+# SPDX-FileCopyrightText: 2022 - 2023 Intel Corporation
+#
+# SPDX-License-Identifier: Apache-2.0
+"""Sycl implementation for gaussian elimination."""
+
+from .gaussian_sycl._gaussian_sycl import gaussian as gaussian_sycl
+
+__all__ = ["gaussian_sycl"]

dpbench/benchmarks/rodinia/gaussian/gaussian_sycl_native_ext/gaussian_sycl/_gaussian_kernel.hpp

@@ -0,0 +1,57 @@
+// SPDX-FileCopyrightText: 2022 - 2023 Intel Corporation
+//
+// SPDX-License-Identifier: Apache-2.0
+#include <CL/sycl.hpp>
+
+using namespace sycl;
+
+template <typename FpTy>
+void gaussian_kernel_1(FpTy *m_device,
+                       const FpTy *a_device,
+                       int size,
+                       int t,
+                       sycl::nd_item<3> item_ct1)
+{
+    if (item_ct1.get_local_id(2) +
+            item_ct1.get_group(2) * item_ct1.get_local_range().get(2) >=
+        size - 1 - t)
+        return;
+    m_device[size * (item_ct1.get_local_range().get(2) * item_ct1.get_group(2) +
+                     item_ct1.get_local_id(2) + t + 1) +
+             t] = a_device[size * (item_ct1.get_local_range().get(2) *
+                                       item_ct1.get_group(2) +
+                                   item_ct1.get_local_id(2) + t + 1) +
+                           t] /
+                  a_device[size * t + t];
+}
+
+template <typename FpTy>
+void gaussian_kernel_2(FpTy *m_device,
+                       FpTy *a_device,
+                       FpTy *b_device,
+                       int size,
+                       int j1,
+                       int t,
+                       sycl::nd_item<3> item_ct1)
+{
+    if (item_ct1.get_local_id(2) +
+            item_ct1.get_group(2) * item_ct1.get_local_range().get(2) >=
+        size - 1 - t)
+        return;
+    if (item_ct1.get_local_id(1) +
+            item_ct1.get_group(1) * item_ct1.get_local_range().get(1) >=
+        size - t)
+        return;
+
+    int xidx = item_ct1.get_group(2) * item_ct1.get_local_range().get(2) +
+               item_ct1.get_local_id(2);
+    int yidx = item_ct1.get_group(1) * item_ct1.get_local_range().get(1) +
+               item_ct1.get_local_id(1);
+
+    a_device[size * (xidx + 1 + t) + (yidx + t)] -=
+        m_device[size * (xidx + 1 + t) + t] * a_device[size * t + (yidx + t)];
+    if (yidx == 0) {
+        b_device[xidx + 1 + t] -=
+            m_device[size * (xidx + 1 + t) + (yidx + t)] * b_device[t];
+    }
+}