Add cuBLAS library migrated samples

Libraries/oneMKL/guided_cuBLAS_examples_SYCL_Migration/01_sycl_dpct_output/Common/cublas_utils.h

@@ -0,0 +1,307 @@
+/*
+ * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *  * Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ *  * Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *  * Neither the name of NVIDIA CORPORATION nor the names of its
+ *    contributors may be used to endorse or promote products derived
+ *    from this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+ * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+ * PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+ * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+ * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+ * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+ * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+ * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ */
+
+#pragma once
+
+#include <sycl/sycl.hpp>
+#include <dpct/dpct.hpp>
+#include <cmath>
+#include <functional>
+#include <iostream>
+#include <random>
+#include <stdexcept>
+#include <string>
+#include <dpct/lib_common_utils.hpp>
+
+#include <complex>
+
+// CUDA API error checking
+/*
+DPCT1001:1: The statement could not be removed.
+*/
+/*
+DPCT1000:2: Error handling if-stmt was detected but could not be rewritten.
+*/
+#define CUDA_CHECK(err)                                                        \
+    do {                                                                       \
+        int err_ = (err);                                                      \
+        if (err_ != 0) {                                                       \
+            std::printf("CUDA error %d at %s:%d\n", err_, __FILE__, __LINE__); \
+            throw std::runtime_error("CUDA error");                            \
+        }                                                                      \
+    } while (0)
+
+// cublas API error checking
+#define CUBLAS_CHECK(err)                                                      \
+    do {                                                                       \
+        int err_ = (err);                                                      \
+        if (err_ != 0) {                                                       \
+            std::printf("cublas error %d at %s:%d\n", err_, __FILE__,          \
+                        __LINE__);                                             \
+            throw std::runtime_error("cublas error");                          \
+        }                                                                      \
+    } while (0)
+
+// memory alignment
+#define ALIGN_TO(A, B) (((A + B - 1) / B) * B)
+
+// device memory pitch alignment
+static const size_t device_alignment = 32;
+
+// type traits
+template <typename T> struct traits;
+
+template <> struct traits<float> {
+    // scalar type
+    typedef float T;
+    typedef T S;
+
+    static constexpr T zero = 0.f;
+    static constexpr dpct::library_data_t cuda_data_type =
+        dpct::library_data_t::real_float;
+
+    inline static S abs(T val) { return fabs(val); }
+
+    template <typename RNG> inline static T rand(RNG &gen) { return (S)gen(); }
+
+    inline static T add(T a, T b) { return a + b; }
+
+    inline static T mul(T v, double f) { return v * f; }
+};
+
+template <> struct traits<double> {
+    // scalar type
+    typedef double T;
+    typedef T S;
+
+    static constexpr T zero = 0.;
+    static constexpr dpct::library_data_t cuda_data_type =
+        dpct::library_data_t::real_double;
+
+    inline static S abs(T val) { return fabs(val); }
+
+    template <typename RNG> inline static T rand(RNG &gen) { return (S)gen(); }
+
+    inline static T add(T a, T b) { return a + b; }
+
+    inline static T mul(T v, double f) { return v * f; }
+};
+
+template <> struct traits<sycl::float2> {
+    // scalar type
+    typedef float S;
+    typedef sycl::float2 T;
+
+    static constexpr T zero = {0.f, 0.f};
+    static constexpr dpct::library_data_t cuda_data_type =
+        dpct::library_data_t::complex_float;
+
+    inline static S abs(T val) { return dpct::cabs<float>(val); }
+
+    template <typename RNG> inline static T rand(RNG &gen) {
+        return sycl::float2((S)gen(), (S)gen());
+    }
+
+    inline static T add(T a, T b) { return a + b; }
+    inline static T add(T a, S b) { return a + sycl::float2(b, 0.f); }
+
+    inline static T mul(T v, double f) {
+        return sycl::float2(v.x() * f, v.y() * f);
+    }
+};
+
+template <> struct traits<sycl::double2> {
+    // scalar type
+    typedef double S;
+    typedef sycl::double2 T;
+
+    static constexpr T zero = {0., 0.};
+    static constexpr dpct::library_data_t cuda_data_type =
+        dpct::library_data_t::complex_double;
+
+    inline static S abs(T val) { return dpct::cabs<double>(val); }
+
+    template <typename RNG> inline static T rand(RNG &gen) {
+        return sycl::double2((S)gen(), (S)gen());
+    }
+
+    inline static T add(T a, T b) { return a + b; }
+    inline static T add(T a, S b) { return a + sycl::double2(b, 0.); }
+
+    inline static T mul(T v, double f) {
+        return sycl::double2(v.x() * f, v.y() * f);
+    }
+};
+
+template <typename T> void print_matrix(const int &m, const int &n, const T *A, const int &lda);
+
+template <> void print_matrix(const int &m, const int &n, const float *A, const int &lda) {
+    for (int i = 0; i < m; i++) {
+        for (int j = 0; j < n; j++) {
+            std::printf("%0.2f ", A[j * lda + i]);
+        }
+        std::printf("\n");
+    }
+}
+
+template <> void print_matrix(const int &m, const int &n, const double *A, const int &lda) {
+    for (int i = 0; i < m; i++) {
+        for (int j = 0; j < n; j++) {
+            std::printf("%0.2f ", A[j * lda + i]);
+        }
+        std::printf("\n");
+    }
+}
+
+template <>
+void print_matrix(const int &m, const int &n, const sycl::float2 *A,
+                  const int &lda) {
+    for (int i = 0; i < m; i++) {
+        for (int j = 0; j < n; j++) {
+            std::printf("%0.2f + %0.2fj ", A[j * lda + i].x(),
+                        A[j * lda + i].y());
+        }
+        std::printf("\n");
+    }
+}
+
+template <>
+void print_matrix(const int &m, const int &n, const sycl::double2 *A,
+                  const int &lda) {
+    for (int i = 0; i < m; i++) {
+        for (int j = 0; j < n; j++) {
+            std::printf("%0.2f + %0.2fj ", A[j * lda + i].x(),
+                        A[j * lda + i].y());
+        }
+        std::printf("\n");
+    }
+}
+
+template <typename T> void print_vector(const int &m, const T *A);
+
+template <> void print_vector(const int &m, const float *A) {
+    for (int i = 0; i < m; i++) {
+        std::printf("%0.2f ", A[i]);
+    }
+    std::printf("\n");
+}
+
+template <> void print_vector(const int &m, const double *A) {
+    for (int i = 0; i < m; i++) {
+        std::printf("%0.2f ", A[i]);
+    }
+    std::printf("\n");
+}
+
+template <> void print_vector(const int &m, const sycl::float2 *A) {
+    for (int i = 0; i < m; i++) {
+        std::printf("%0.2f + %0.2fj ", A[i].x(), A[i].y());
+    }
+    std::printf("\n");
+}
+
+template <> void print_vector(const int &m, const sycl::double2 *A) {
+    for (int i = 0; i < m; i++) {
+        std::printf("%0.2f + %0.2fj ", A[i].x(), A[i].y());
+    }
+    std::printf("\n");
+}
+
+template <typename T> void generate_random_matrix(int m, int n, T **A, int *lda) {
+    std::random_device rd;
+    std::mt19937 gen(rd());
+    std::uniform_real_distribution<typename traits<T>::S> dis(-1.0, 1.0);
+    auto rand_gen = std::bind(dis, gen);
+
+    *lda = n;
+
+    size_t matrix_mem_size = static_cast<size_t>(*lda * m * sizeof(T));
+    // suppress gcc 7 size warning
+    if (matrix_mem_size <= PTRDIFF_MAX)
+        *A = (T *)malloc(matrix_mem_size);
+    else
+        throw std::runtime_error("Memory allocation size is too large");
+
+    if (*A == NULL)
+        throw std::runtime_error("Unable to allocate host matrix");
+
+    // random matrix and accumulate row sums
+    for (int i = 0; i < m; ++i) {
+        for (int j = 0; j < n; ++j) {
+            T *A_row = (*A) + *lda * i;
+            A_row[j] = traits<T>::rand(rand_gen);
+        }
+    }
+}
+
+// Makes matrix A of size mxn and leading dimension lda diagonal dominant
+template <typename T> void make_diag_dominant_matrix(int m, int n, T *A, int lda) {
+    for (int i = 0; i < std::min(m, n); ++i) {
+        T *A_row = A + lda * i;
+        auto row_sum = traits<typename traits<T>::S>::zero;
+        for (int j = 0; j < n; ++j) {
+            row_sum += traits<T>::abs(A_row[j]);
+        }
+        A_row[i] = traits<T>::add(A_row[i], row_sum);
+    }
+}
+
+// Returns cudaDataType value as defined in library_types.h for the string
+// containing type name
+dpct::library_data_t get_cuda_library_type(std::string type_string) {
+    if (type_string.compare("CUDA_R_16F") == 0)
+        return dpct::library_data_t::real_half;
+    else if (type_string.compare("CUDA_C_16F") == 0)
+        return dpct::library_data_t::complex_half;
+    else if (type_string.compare("CUDA_R_32F") == 0)
+        return dpct::library_data_t::real_float;
+    else if (type_string.compare("CUDA_C_32F") == 0)
+        return dpct::library_data_t::complex_float;
+    else if (type_string.compare("CUDA_R_64F") == 0)
+        return dpct::library_data_t::real_double;
+    else if (type_string.compare("CUDA_C_64F") == 0)
+        return dpct::library_data_t::complex_double;
+    else if (type_string.compare("CUDA_R_8I") == 0)
+        return dpct::library_data_t::real_int8;
+    else if (type_string.compare("CUDA_C_8I") == 0)
+        return dpct::library_data_t::complex_int8;
+    else if (type_string.compare("CUDA_R_8U") == 0)
+        return dpct::library_data_t::real_uint8;
+    else if (type_string.compare("CUDA_C_8U") == 0)
+        return dpct::library_data_t::complex_uint8;
+    else if (type_string.compare("CUDA_R_32I") == 0)
+        return dpct::library_data_t::real_int32;
+    else if (type_string.compare("CUDA_C_32I") == 0)
+        return dpct::library_data_t::complex_int32;
+    else if (type_string.compare("CUDA_R_32U") == 0)
+        return dpct::library_data_t::real_uint32;
+    else if (type_string.compare("CUDA_C_32U") == 0)
+        return dpct::library_data_t::complex_uint32;
+    else
+        throw std::runtime_error("Unknown CUDA datatype");
+}