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/*
* 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 <cmath>
#include <functional>
#include <iostream>
#include <random>
#include <stdexcept>
#include <string>
#include <cuComplex.h>
#include <cuda_runtime_api.h>
#include <cublas_api.h>
#include <cusolverDn.h>
#include <library_types.h>
// CUDA API error checking
#define CUDA_CHECK(err) \
do { \
cudaError_t err_ = (err); \
if (err_ != cudaSuccess) { \
printf("CUDA error %d at %s:%d\n", err_, __FILE__, __LINE__); \
throw std::runtime_error("CUDA error"); \
} \
} while (0)
// cusolver API error checking
#define CUSOLVER_CHECK(err) \
do { \
cusolverStatus_t err_ = (err); \
if (err_ != CUSOLVER_STATUS_SUCCESS) { \
printf("cusolver error %d at %s:%d\n", err_, __FILE__, __LINE__); \
throw std::runtime_error("cusolver error"); \
} \
} while (0)
// cublas API error checking
#define CUBLAS_CHECK(err) \
do { \
cublasStatus_t err_ = (err); \
if (err_ != CUBLAS_STATUS_SUCCESS) { \
printf("cublas error %d at %s:%d\n", err_, __FILE__, __LINE__); \
throw std::runtime_error("cublas error"); \
} \
} while (0)
// cublas API error checking
#define CUSPARSE_CHECK(err) \
do { \
cusparseStatus_t err_ = (err); \
if (err_ != CUSPARSE_STATUS_SUCCESS) { \
printf("cusparse error %d at %s:%d\n", err_, __FILE__, __LINE__); \
throw std::runtime_error("cusparse 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 cudaDataType cuda_data_type = CUDA_R_32F;
#if CUDART_VERSION >= 11000
static constexpr cusolverPrecType_t cusolver_precision_type = CUSOLVER_R_32F;
#endif
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 cudaDataType cuda_data_type = CUDA_R_64F;
#if CUDART_VERSION >= 11000
static constexpr cusolverPrecType_t cusolver_precision_type = CUSOLVER_R_64F;
#endif
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<cuFloatComplex> {
// scalar type
typedef float S;
typedef cuFloatComplex T;
static constexpr T zero = {0.f, 0.f};
static constexpr cudaDataType cuda_data_type = CUDA_C_32F;
#if CUDART_VERSION >= 11000
static constexpr cusolverPrecType_t cusolver_precision_type = CUSOLVER_C_32F;
#endif
inline static S abs(T val) { return cuCabsf(val); }
template <typename RNG> inline static T rand(RNG &gen) {
return make_cuFloatComplex((S)gen(), (S)gen());
}
inline static T add(T a, T b) { return cuCaddf(a, b); }
inline static T add(T a, S b) { return cuCaddf(a, make_cuFloatComplex(b, 0.f)); }
inline static T mul(T v, double f) { return make_cuFloatComplex(v.x * f, v.y * f); }
};
template <> struct traits<cuDoubleComplex> {
// scalar type
typedef double S;
typedef cuDoubleComplex T;
static constexpr T zero = {0., 0.};
static constexpr cudaDataType cuda_data_type = CUDA_C_64F;
#if CUDART_VERSION >= 11000
static constexpr cusolverPrecType_t cusolver_precision_type = CUSOLVER_C_64F;
#endif
inline static S abs(T val) { return cuCabs(val); }
template <typename RNG> inline static T rand(RNG &gen) {
return make_cuDoubleComplex((S)gen(), (S)gen());
}
inline static T add(T a, T b) { return cuCadd(a, b); }
inline static T add(T a, S b) { return cuCadd(a, make_cuDoubleComplex(b, 0.)); }
inline static T mul(T v, double f) { return make_cuDoubleComplex(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++) {
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}
std::printf("\n");
}
}
template <> void print_matrix(const int &m, const int &n, const cuComplex *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 cuDoubleComplex *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 generate_random_matrix(cusolver_int_t m, cusolver_int_t 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(cusolver_int_t m, cusolver_int_t 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
// cudaDataType get_cuda_library_type(std::string type_string) {
// if (type_string.compare("CUDA_R_16F") == 0)
// return CUDA_R_16F;
// else if (type_string.compare("CUDA_C_16F") == 0)
// return CUDA_C_16F;
// else if (type_string.compare("CUDA_R_32F") == 0)
// return CUDA_R_32F;
// else if (type_string.compare("CUDA_C_32F") == 0)
// return CUDA_C_32F;
// else if (type_string.compare("CUDA_R_64F") == 0)
// return CUDA_R_64F;
// else if (type_string.compare("CUDA_C_64F") == 0)
// return CUDA_C_64F;
// else if (type_string.compare("CUDA_R_8I") == 0)
// return CUDA_R_8I;
// else if (type_string.compare("CUDA_C_8I") == 0)
// return CUDA_C_8I;
// else if (type_string.compare("CUDA_R_8U") == 0)
// return CUDA_R_8U;
// else if (type_string.compare("CUDA_C_8U") == 0)
// return CUDA_C_8U;
// else if (type_string.compare("CUDA_R_32I") == 0)
// return CUDA_R_32I;
// else if (type_string.compare("CUDA_C_32I") == 0)
// return CUDA_C_32I;
// else if (type_string.compare("CUDA_R_32U") == 0)
// return CUDA_R_32U;
// else if (type_string.compare("CUDA_C_32U") == 0)
// return CUDA_C_32U;
// else
// throw std::runtime_error("Unknown CUDA datatype");
// }
// // Returns cusolverIRSRefinement_t value as defined in cusolver_common.h for the string containing
// // solver name
// cusolverIRSRefinement_t get_cusolver_refinement_solver(std::string solver_string) {
// if (solver_string.compare("CUSOLVER_IRS_REFINE_NONE") == 0)
// return CUSOLVER_IRS_REFINE_NONE;
// else if (solver_string.compare("CUSOLVER_IRS_REFINE_CLASSICAL") == 0)
// return CUSOLVER_IRS_REFINE_CLASSICAL;
// else if (solver_string.compare("CUSOLVER_IRS_REFINE_GMRES") == 0)
// return CUSOLVER_IRS_REFINE_GMRES;
// else if (solver_string.compare("CUSOLVER_IRS_REFINE_CLASSICAL_GMRES") == 0)
// return CUSOLVER_IRS_REFINE_CLASSICAL_GMRES;
// else if (solver_string.compare("CUSOLVER_IRS_REFINE_GMRES_GMRES") == 0)
// return CUSOLVER_IRS_REFINE_GMRES_GMRES;
// else
// printf("Unknown solver parameter: \"%s\"\n", solver_string.c_str());
// return CUSOLVER_IRS_REFINE_NOT_SET;
// }