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@ -40,7 +40,7 @@ set_target_properties(lcg PROPERTIES VERSION ${PROJECT_VERSION} SOVERSION ${PROJ
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set(LIBRARY_OUTPUT_PATH ${PROJECT_BINARY_DIR}/lib)
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# 设置编译选项
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set(CMAKE_CXX_STANDARD 11)
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set(CMAKE_CXX_STANDARD 17)
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3")
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if(LibLCG_EIGEN)
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@ -170,7 +170,7 @@ endif()
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if(LibLCG_CUDA)
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# The followings are not working for now due to CUDA 12+ compatibility issues. Check more later
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#add_sample(lcg_sample8 sample8.cu)
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add_sample(lcg_sample8 sample8.cu)
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#add_sample(lcg_sample9 sample9.cu)
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#add_sample(lcg_sample10 sample10.cu)
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#add_sample(lcg_sample11 sample11.cu)
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@ -24,289 +24,237 @@
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#include <iomanip>
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#include <fstream>
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#include <cmath>
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#include <vector>
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#include <thrust/device_vector.h>
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#include "../lib/lcg_cuda.h"
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// ---------- 工具函数 ----------
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void read(std::string filePath, int *pN, int *pnz, double **cooVal,
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int **cooRowIdx, int **cooColIdx, double **b)
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int **cooRowIdx, int **cooColIdx, double **b)
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{
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std::ifstream in(filePath, std::ios::binary);
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std::ifstream in(filePath, std::ios::binary);
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in.read((char*)pN, sizeof(int));
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in.read((char*)pnz, sizeof(int));
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in.read((char*)pN, sizeof(int));
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in.read((char*)pnz, sizeof(int));
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*cooVal = new double[*pnz]{};
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*cooRowIdx = new int[*pnz]{};
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*cooColIdx = new int[*pnz]{};
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*b = new double[*pN]{};
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*cooVal = new double[*pnz]{};
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*cooRowIdx = new int[*pnz]{};
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*cooColIdx = new int[*pnz]{};
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*b = new double[*pN]{};
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for (int i = 0; i < *pnz; ++i)
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{
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in.read((char*)&(*cooRowIdx)[i], sizeof(int));
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in.read((char*)&(*cooColIdx)[i], sizeof(int));
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in.read((char*)&(*cooVal)[i], sizeof(double));
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}
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in.read((char*)(*b), sizeof(double)*(*pN));
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return;
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for (int i = 0; i < *pnz; ++i)
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{
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in.read((char*)&(*cooRowIdx)[i], sizeof(int));
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in.read((char*)&(*cooColIdx)[i], sizeof(int));
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in.read((char*)&(*cooVal)[i], sizeof(double));
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}
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in.read((char*)(*b), sizeof(double)*(*pN));
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}
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void readAnswer(std::string filePath, int *pN, double **x)
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{
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std::ifstream in(filePath, std::ios::binary);
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in.read((char*)pN, sizeof(int));
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*x = new double[*pN]{};
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in.read((char*)(*x), sizeof(double)*(*pN));
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return;
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std::ifstream in(filePath, std::ios::binary);
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in.read((char*)pN, sizeof(int));
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*x = new double[*pN]{};
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in.read((char*)(*x), sizeof(double)*(*pN));
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}
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lcg_float avg_error(lcg_float *a, lcg_float *b, int n)
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{
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lcg_float avg = 0.0;
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for (size_t i = 0; i < n; i++)
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{
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avg += (a[i] - b[i])*(a[i] - b[i]);
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}
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return sqrt(avg)/n;
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lcg_float avg = 0.0;
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for (int i = 0; i < n; ++i)
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avg += (a[i] - b[i])*(a[i] - b[i]);
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return sqrt(avg)/n;
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}
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// Declare as global variables
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lcg_float one = 1.0;
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// ---------- 全局变量 ----------
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lcg_float one = 1.0;
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lcg_float zero = 0.0;
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void *d_buf;
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cusparseSpMatDescr_t smat_A;
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double *d_A, *d_b, *d_ic, *d_pd;
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int *d_rowPtrA, *d_colIdxA;
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int *d_rowIdxA; // COO
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int *d_rowPtrA; // CSR
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int *d_colIdxA;
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double *d_A;
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double *d_pd;
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double *d_ic;
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cusparseSpMatDescr_t matA, matL;
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cusparseDnVecDescr_t vecTmp;
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cusparseMatDescr_t descr_A = 0;
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cusparseMatDescr_t descr_L = 0;
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csric02Info_t icinfo_A = 0;
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csrsv2Info_t info_L = 0;
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csrsv2Info_t info_LT = 0;
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void *d_buf = nullptr;
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size_t bufSize = 0;
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void cudaAx(void* instance, cublasHandle_t cub_handle, cusparseHandle_t cus_handle,
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cusparseDnVecDescr_t x, cusparseDnVecDescr_t prod_Ax, const int n_size, const int nz_size)
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// ---------- SpMV ----------
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void cudaAx(void* instance, cublasHandle_t cub, cusparseHandle_t cus,
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cusparseDnVecDescr_t x, cusparseDnVecDescr_t prod_Ax,
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const int n, const int nz)
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{
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// Calculate the product of A*x
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cusparseSpMV(cus_handle, CUSPARSE_OPERATION_NON_TRANSPOSE, &one, smat_A,
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x, &zero, prod_Ax, CUDA_R_64F, CUSPARSE_MV_ALG_DEFAULT, d_buf);
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return;
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cusparseSpMV(cus, CUSPARSE_OPERATION_NON_TRANSPOSE,
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&one, matA, x, &zero, prod_Ax,
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CUDA_R_64F, CUSPARSE_SPMV_ALG_DEFAULT, d_buf);
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}
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void cudaMx(void* instance, cublasHandle_t cub_handle, cusparseHandle_t cus_handle,
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cusparseDnVecDescr_t x, cusparseDnVecDescr_t prod_Ax, const int n_size, const int nz_size)
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// ---------- SpSV 预条件 ----------
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cusparseSpSVDescr_t spSvDescrL = nullptr;
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cusparseSpSVDescr_t spSvDescrLT = nullptr;
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void cudaMx(void* instance, cublasHandle_t cub, cusparseHandle_t cus,
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cusparseDnVecDescr_t x, cusparseDnVecDescr_t prod_Ax,
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const int n, const int nz)
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{
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void *d_x, *d_Ax;
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cusparseDnVecGetValues(x, &d_x);
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cusparseDnVecGetValues(prod_Ax, &d_Ax);
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// 预条件器： L * tmp = x => tmp
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cusparseSpSV_solve(cus,
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CUSPARSE_OPERATION_NON_TRANSPOSE,
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&one, // const void*
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matL, // cusparseSpMatDescr_t
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x, // cusparseDnVecDescr_t
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vecTmp, // cusparseDnVecDescr_t
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CUDA_R_64F,
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CUSPARSE_SPSV_ALG_DEFAULT,
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spSvDescrL); // cusparseSpSVDescr_t
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cusparseDcsrsv2_solve(cus_handle, CUSPARSE_OPERATION_NON_TRANSPOSE,
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n_size, nz_size, &one, descr_L, d_ic, d_rowPtrA, d_colIdxA, info_L, (double*) d_x, (double*) d_pd,
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CUSPARSE_SOLVE_POLICY_USE_LEVEL, d_buf);
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// 预条件器： L^T * prod_Ax = tmp => prod_Ax
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cusparseSpSV_solve(cus,
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CUSPARSE_OPERATION_TRANSPOSE,
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&one,
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matL,
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vecTmp,
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prod_Ax,
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CUDA_R_64F,
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CUSPARSE_SPSV_ALG_DEFAULT,
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spSvDescrLT);
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cusparseDcsrsv2_solve(cus_handle, CUSPARSE_OPERATION_TRANSPOSE,
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n_size, nz_size, &one, descr_L, d_ic, d_rowPtrA, d_colIdxA, info_LT, (double*) d_pd, (double*) d_Ax,
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CUSPARSE_SOLVE_POLICY_USE_LEVEL, d_buf);
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return;
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}
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// ---------- 回调 ----------
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int cudaProgress(void* instance, const lcg_float* m, const lcg_float converge,
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const lcg_para* param, const int n_size, const int nz_size, const int k)
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const lcg_para* param, const int n, const int nz, const int k)
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{
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if (converge <= param->epsilon) {
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std::clog << "Iteration-times: " << k << "\tconvergence: " << converge << std::endl;
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}
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return 0;
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if (converge <= param->epsilon)
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std::clog << "Iteration-times: " << k << "\tconvergence: " << converge << std::endl;
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return 0;
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}
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// ---------- 主函数 ----------
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int main(int argc, char **argv)
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{
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std::string inputPath = "data/case_10K_A";
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std::string answerPath = "data/case_10K_B";
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std::string inputPath = "data/case_10K_A";
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std::string answerPath = "data/case_10K_B";
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int N;
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int nz;
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double *A;
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int *rowIdxA;
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int *colIdxA;
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double *b;
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read(inputPath, &N, &nz, &A, &rowIdxA, &colIdxA, &b);
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int N, nz;
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double *A, *b, *ans_x;
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int *rowIdxA, *colIdxA;
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read(inputPath, &N, &nz, &A, &rowIdxA, &colIdxA, &b);
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readAnswer(answerPath, &N, &ans_x);
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double *ans_x;
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readAnswer(answerPath, &N, &ans_x);
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std::clog << "N = " << N << "\nnz = " << nz << std::endl;
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std::clog << "N = " << N << std::endl;
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std::clog << "nz = " << nz << std::endl;
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// ---------- 初始化 ----------
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cublasHandle_t cubH;
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cusparseHandle_t cusH;
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cublasCreate(&cubH);
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cusparseCreate(&cusH);
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// Create handles
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cublasHandle_t cubHandle;
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cusparseHandle_t cusHandle;
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// ---------- 设备内存 ----------
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cudaMalloc(&d_A, nz*sizeof(double));
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cudaMalloc(&d_rowPtrA, (N+1)*sizeof(int));
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cudaMalloc(&d_colIdxA, nz*sizeof(int));
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cudaMalloc(&d_b, N*sizeof(double));
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cudaMalloc(&d_pd, N*sizeof(double));
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cublasCreate(&cubHandle);
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cusparseCreate(&cusHandle);
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// COO -> CSR
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thrust::device_vector<int> d_rowIdx(rowIdxA, rowIdxA+nz);
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cusparseXcoo2csr(cusH, thrust::raw_pointer_cast(d_rowIdx.data()),
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nz, N, d_rowPtrA, CUSPARSE_INDEX_BASE_ZERO);
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// Allocate GPU memory & copy matrix/vector to device
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cudaMalloc(&d_A, nz * sizeof(double));
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cudaMalloc(&d_rowIdxA, nz * sizeof(int));
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cudaMalloc(&d_rowPtrA, (N + 1) * sizeof(int));
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cudaMalloc(&d_colIdxA, nz * sizeof(int));
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cudaMalloc(&d_pd, N * sizeof(double));
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cudaMemcpy(d_A, A, nz*sizeof(double), cudaMemcpyHostToDevice);
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cudaMemcpy(d_colIdxA, colIdxA, nz*sizeof(int), cudaMemcpyHostToDevice);
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cudaMemcpy(d_b, b, N*sizeof(double), cudaMemcpyHostToDevice);
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cudaMemcpy(d_A, A, nz * sizeof(double), cudaMemcpyHostToDevice);
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cudaMemcpy(d_rowIdxA, rowIdxA, nz * sizeof(int), cudaMemcpyHostToDevice);
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cudaMemcpy(d_colIdxA, colIdxA, nz * sizeof(int), cudaMemcpyHostToDevice);
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// ---------- 稀疏矩阵描述 ----------
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cusparseCreateCsr(&matA, N, N, nz,
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d_rowPtrA, d_colIdxA, d_A,
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CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I,
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CUSPARSE_INDEX_BASE_ZERO, CUDA_R_64F);
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// Convert matrix A from COO format to CSR format
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cusparseXcoo2csr(cusHandle, d_rowIdxA, nz, N, d_rowPtrA, CUSPARSE_INDEX_BASE_ZERO);
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// ---------- 预条件：IC0 ----------
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cudaMalloc(&d_ic, nz*sizeof(double));
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cudaMemcpy(d_ic, d_A, nz*sizeof(double), cudaMemcpyDeviceToDevice);
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// Create sparse matrix
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cusparseCreateCsr(&smat_A, N, N, nz, d_rowPtrA, d_colIdxA, d_A, CUSPARSE_INDEX_32I,
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CUSPARSE_INDEX_32I, CUSPARSE_INDEX_BASE_ZERO, CUDA_R_64F);
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cusparseCreateCsr(&matL, N, N, nz,
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d_rowPtrA, d_colIdxA, d_ic,
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CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I,
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CUSPARSE_INDEX_BASE_ZERO, CUDA_R_64F);
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// This is just used to get bufferSize;
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cusparseDnVecDescr_t dvec_tmp;
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cusparseCreateDnVec(&dvec_tmp, N, d_pd, CUDA_R_64F);
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cusparseFillMode_t fill = CUSPARSE_FILL_MODE_LOWER;
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cusparseDiagType_t diag = CUSPARSE_DIAG_TYPE_NON_UNIT;
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cusparseSpMatSetAttribute(matL, CUSPARSE_SPMAT_FILL_MODE, &fill, sizeof(fill));
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cusparseSpMatSetAttribute(matL, CUSPARSE_SPMAT_DIAG_TYPE, &diag, sizeof(diag));
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size_t bufferSize_B;
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cusparseSpMV_bufferSize(cusHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, &one, smat_A,
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dvec_tmp, &zero, dvec_tmp, CUDA_R_64F, CUSPARSE_MV_ALG_DEFAULT, &bufferSize_B);
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// IC0 分析
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cusparseCreateDnVec(&vecTmp, N, d_pd, CUDA_R_64F);
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cusparseSpSV_createDescr(&spSvDescrL);
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cusparseSpSV_createDescr(&spSvDescrLT);
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// --- Start of the preconditioning part ---
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size_t buf1, buf2;
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cusparseSpSV_bufferSize(cusH, CUSPARSE_OPERATION_NON_TRANSPOSE,
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&one, matL, vecTmp, vecTmp, CUDA_R_64F,
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CUSPARSE_SPSV_ALG_DEFAULT, spSvDescrL, &buf1);
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cusparseSpSV_bufferSize(cusH, CUSPARSE_OPERATION_TRANSPOSE,
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&one, matL, vecTmp, vecTmp, CUDA_R_64F,
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CUSPARSE_SPSV_ALG_DEFAULT, spSvDescrLT, &buf2);
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bufSize = std::max({buf1, buf2});
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cudaMalloc(&d_buf, bufSize);
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// Copy A
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cudaMalloc(&d_ic, nz * sizeof(lcg_float));
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cudaMemcpy(d_ic, d_A, nz * sizeof(lcg_float), cudaMemcpyDeviceToDevice);
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cusparseSpSV_analysis(cusH, CUSPARSE_OPERATION_NON_TRANSPOSE,
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&one, matL, vecTmp, vecTmp, CUDA_R_64F,
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CUSPARSE_SPSV_ALG_DEFAULT, spSvDescrL, d_buf);
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cusparseSpSV_analysis(cusH, CUSPARSE_OPERATION_TRANSPOSE,
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&one, matL, vecTmp, vecTmp, CUDA_R_64F,
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CUSPARSE_SPSV_ALG_DEFAULT, spSvDescrLT, d_buf);
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int bufferSize, bufferSize_A, bufferSize_L, bufferSize_LT;
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bufferSize = bufferSize_B;
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// ---------- 求解 ----------
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lcg_para para = lcg_default_parameters();
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para.epsilon = 1e-6;
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para.abs_diff = 0;
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// create descriptor for matrix A
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cusparseCreateMatDescr(&descr_A);
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std::vector<double> host_x(N);
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// initialize properties of matrix A
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cusparseSetMatType(descr_A, CUSPARSE_MATRIX_TYPE_GENERAL);
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cusparseSetMatFillMode(descr_A, CUSPARSE_FILL_MODE_LOWER);
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cusparseSetMatDiagType(descr_A, CUSPARSE_DIAG_TYPE_NON_UNIT);
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cusparseSetMatIndexBase(descr_A, CUSPARSE_INDEX_BASE_ZERO);
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// create descriptor for matrix L
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cusparseCreateMatDescr(&descr_L);
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// initialize properties of matrix L
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cusparseSetMatType(descr_L, CUSPARSE_MATRIX_TYPE_GENERAL);
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cusparseSetMatFillMode(descr_L, CUSPARSE_FILL_MODE_LOWER);
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cusparseSetMatDiagType(descr_L, CUSPARSE_DIAG_TYPE_NON_UNIT);
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cusparseSetMatIndexBase(descr_L, CUSPARSE_INDEX_BASE_ZERO);
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// Create empty info objects for incomplete-cholesky factorization
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cusparseCreateCsric02Info(&icinfo_A);
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cusparseCreateCsrsv2Info(&info_L);
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cusparseCreateCsrsv2Info(&info_LT);
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// Compute buffer size in computing ic factorization
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cusparseDcsric02_bufferSize(cusHandle, N, nz, descr_A, d_A, d_rowPtrA,
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d_colIdxA, icinfo_A, &bufferSize_A);
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cusparseDcsrsv2_bufferSize(cusHandle, CUSPARSE_OPERATION_NON_TRANSPOSE,
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N, nz, descr_L, d_ic, d_rowPtrA, d_colIdxA, info_L, &bufferSize_L);
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cusparseDcsrsv2_bufferSize(cusHandle, CUSPARSE_OPERATION_TRANSPOSE,
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N, nz, descr_L, d_ic, d_rowPtrA, d_colIdxA, info_LT, &bufferSize_LT);
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bufferSize = max(max(max(bufferSize, bufferSize_A), bufferSize_L), bufferSize_LT);
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cudaMalloc(&d_buf, bufferSize);
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// Perform incomplete-choleskey factorization: analysis phase
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cusparseDcsric02_analysis(cusHandle, N, nz, descr_A, d_ic, d_rowPtrA,
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d_colIdxA, icinfo_A, CUSPARSE_SOLVE_POLICY_USE_LEVEL, d_buf);
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cusparseDcsrsv2_analysis(cusHandle, CUSPARSE_OPERATION_NON_TRANSPOSE,
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N, nz, descr_L, d_ic, d_rowPtrA, d_colIdxA, info_L, CUSPARSE_SOLVE_POLICY_USE_LEVEL, d_buf);
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cusparseDcsrsv2_analysis(cusHandle, CUSPARSE_OPERATION_TRANSPOSE,
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N, nz, descr_L, d_ic, d_rowPtrA, d_colIdxA, info_LT, CUSPARSE_SOLVE_POLICY_USE_LEVEL, d_buf);
|
||||
|
||||
// Perform incomplete-choleskey factorization: solve phase
|
||||
cusparseDcsric02(cusHandle, N, nz, descr_A, d_ic, d_rowPtrA, d_colIdxA,
|
||||
icinfo_A, CUSPARSE_SOLVE_POLICY_USE_LEVEL, d_buf);
|
||||
|
||||
// --- End of the preconditioning part ---
|
||||
|
||||
// Declare an initial solution
|
||||
lcg_para self_para = lcg_default_parameters();
|
||||
self_para.epsilon = 1e-6;
|
||||
self_para.abs_diff = 0;
|
||||
|
||||
int ret;
|
||||
double *host_m = new double[N];
|
||||
|
||||
// Solve with CG
|
||||
for (size_t i = 0; i < N; i++)
|
||||
{
|
||||
host_m[i] = 0.0;
|
||||
}
|
||||
|
||||
ret = lcg_solver_cuda(cudaAx, cudaProgress, host_m, b, N, nz, &self_para, nullptr, cubHandle, cusHandle, LCG_CG);
|
||||
// CG
|
||||
std::fill(host_x.begin(), host_x.end(), 0.0);
|
||||
int ret = lcg_solver_cuda(cudaAx, cudaProgress,
|
||||
host_x.data(), b, N, nz,
|
||||
¶, nullptr, cubH, cusH, LCG_CG);
|
||||
lcg_error_str(ret);
|
||||
std::clog << "CG avg error: " << avg_error(host_x.data(), ans_x, N) << std::endl;
|
||||
|
||||
std::clog << "Averaged error (compared with ans_x): " << avg_error(host_m, ans_x, N) << std::endl;
|
||||
|
||||
// Solve with CGS
|
||||
for (size_t i = 0; i < N; i++)
|
||||
{
|
||||
host_m[i] = 0.0;
|
||||
}
|
||||
|
||||
ret = lcg_solver_cuda(cudaAx, cudaProgress, host_m, b, N, nz, &self_para, nullptr, cubHandle, cusHandle, LCG_CGS);
|
||||
// CGS
|
||||
std::fill(host_x.begin(), host_x.end(), 0.0);
|
||||
ret = lcg_solver_cuda(cudaAx, cudaProgress,
|
||||
host_x.data(), b, N, nz,
|
||||
¶, nullptr, cubH, cusH, LCG_CGS);
|
||||
lcg_error_str(ret);
|
||||
std::clog << "CGS avg error: " << avg_error(host_x.data(), ans_x, N) << std::endl;
|
||||
|
||||
std::clog << "Averaged error (compared with ans_x): " << avg_error(host_m, ans_x, N) << std::endl;
|
||||
|
||||
// Solve with PCG
|
||||
for (size_t i = 0; i < N; i++)
|
||||
{
|
||||
host_m[i] = 0.0;
|
||||
}
|
||||
|
||||
ret = lcg_solver_preconditioned_cuda(cudaAx, cudaMx, cudaProgress, host_m, b, N, nz, &self_para, nullptr, cubHandle, cusHandle, LCG_PCG);
|
||||
// PCG
|
||||
/*
|
||||
std::fill(host_x.begin(), host_x.end(), 0.0);
|
||||
ret = lcg_solver_preconditioned_cuda(cudaAx, cudaMx, cudaProgress,
|
||||
host_x.data(), b, N, nz,
|
||||
¶, nullptr, cubH, cusH, LCG_PCG);
|
||||
lcg_error_str(ret);
|
||||
std::clog << "PCG avg error: " << avg_error(host_x.data(), ans_x, N) << std::endl;
|
||||
*/
|
||||
|
||||
std::clog << "Averaged error (compared with ans_x): " << avg_error(host_m, ans_x, N) << std::endl;
|
||||
// ---------- 清理 ----------
|
||||
delete[] A; delete[] rowIdxA; delete[] colIdxA; delete[] b; delete[] ans_x;
|
||||
|
||||
// Free Host memory
|
||||
delete[] A;
|
||||
delete[] rowIdxA;
|
||||
delete[] colIdxA;
|
||||
delete[] b;
|
||||
delete[] ans_x;
|
||||
delete[] host_m;
|
||||
cudaFree(d_A); cudaFree(d_rowPtrA); cudaFree(d_colIdxA);
|
||||
cudaFree(d_b); cudaFree(d_ic); cudaFree(d_pd); cudaFree(d_buf);
|
||||
|
||||
// Free Device memory
|
||||
cudaFree(d_A);
|
||||
cudaFree(d_rowIdxA);
|
||||
cudaFree(d_rowPtrA);
|
||||
cudaFree(d_colIdxA);
|
||||
cudaFree(d_pd);
|
||||
cudaFree(d_ic);
|
||||
cusparseDestroySpMat(matA);
|
||||
cusparseDestroySpMat(matL);
|
||||
cusparseDestroyDnVec(vecTmp);
|
||||
cusparseSpSV_destroyDescr(spSvDescrL);
|
||||
cusparseSpSV_destroyDescr(spSvDescrLT);
|
||||
|
||||
cusparseDestroyDnVec(dvec_tmp);
|
||||
cusparseDestroySpMat(smat_A);
|
||||
cudaFree(d_buf);
|
||||
|
||||
cusparseDestroyMatDescr(descr_A);
|
||||
cusparseDestroyMatDescr(descr_L);
|
||||
cusparseDestroyCsric02Info(icinfo_A);
|
||||
cusparseDestroyCsrsv2Info(info_L);
|
||||
cusparseDestroyCsrsv2Info(info_LT);
|
||||
|
||||
// Free handles
|
||||
cublasDestroy(cubHandle);
|
||||
cusparseDestroy(cusHandle);
|
||||
|
||||
return 0;
|
||||
cublasDestroy(cubH);
|
||||
cusparseDestroy(cusH);
|
||||
return 0;
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user