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#include <stdio.h>
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#include <arm_neon.h>
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#include <ctime>
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#include <stdlib.h>
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#define M 27
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#define N 25
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#define Q 12
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//A(M*N),B(N*Q),C(M*Q),transposed(Q*N)对于矩阵维度的说明
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void transposeMatrix(float** matrix, float** transposed) {
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for (int i = 0; i < N; i++) {
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for (int j = 0; j < Q; j++) {
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transposed[j][i] = matrix[i][j];
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}
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}
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}
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void matmul_optimized(float** A, float** B, float** C) {
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for (int i = 0; i < M; i++) {
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for (int j = 0; j < Q; j++) {
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float sum = 0.0f;
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for (int k = 0; k < N; k += 4)
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{
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float32x4_t vecA, vecB, vecC;
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if (k + 4 <= N)
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{
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// 加载A和B的4个元素，进行向量化计算
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vecA = vld1q_f32(&A[i][k]);
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vecB = vld1q_f32(&B[j][k]);
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// 向量化乘法并累加结果
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vecC = vmulq_f32(vecA, vecB);
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sum += vgetq_lane_f32(vecC, 0) + vgetq_lane_f32(vecC, 1) +
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vgetq_lane_f32(vecC, 2) + vgetq_lane_f32(vecC, 3);
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}
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else
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{
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// 处理剩余的元素
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for (int m = k; m < N; m++)
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{
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sum += A[i][m] * B[j][m];
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}
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}
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}
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C[i][j] = sum;
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}
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}
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}
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int main()
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{
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//矩阵 A 的 COO 格式
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float A_values[] = {1, 2, 3,4,5};
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int A_rowIndex[] = {0, 0, 1, 2, 2};
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int A_colIndex[] = {0, 2, 1,0, 2};
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int A_nonZeroCount = 5;
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// 矩阵 B 的 COO 格式
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float B_values[] = {6,8,7,9};
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int B_rowIndex[] = {0,2, 1, 2};
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int B_colIndex[] ={0,0,1, 2};
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int B_nonZeroCount=4;
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//动态分配内存
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float** denseMatrixA = (float**)malloc(M * sizeof(float*));
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float** denseMatrixB = (float**)malloc(N * sizeof(float*));
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float** C = (float**)malloc(M * sizeof(float*));
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float** transposed = (float**)malloc(Q * sizeof(float*));
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for (int i = 0; i < M; i++) {
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denseMatrixA[i] = (float*)malloc(N * sizeof(float));
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C[i] = (float*)malloc(Q * sizeof(float));
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}
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for (int i = 0; i < N; ++i)
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{
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denseMatrixB[i] = (float*)malloc(Q * sizeof(float));
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}
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for (int i = 0; i < Q; ++i)
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{
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transposed[i] = (float*)malloc(N * sizeof(float));
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}
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// 实现稀疏矩阵转换为普通矩阵
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for(int i=0;i<M;i++)
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{
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for(int j=0;j<N;j++)
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{
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denseMatrixA[i][j]=0;
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}
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}
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for(int i=0;i<N;i++)
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{
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for(int j=0;j<Q;j++)
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{
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denseMatrixB[i][j]=0;
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}
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}
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for(int i = 0; i < A_nonZeroCount; i++)
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{
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int row = A_rowIndex[i];
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int col = A_colIndex[i];
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denseMatrixA[row][col] = A_values[i];
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}
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for(int i = 0; i < B_nonZeroCount; i++)
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{
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int row = B_rowIndex[i];
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int col = B_colIndex[i];
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denseMatrixB[row][col] = B_values[i];
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}
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//打印常规矩阵
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printf("A的常规矩阵为：\n");
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for(int i=0;i<M;i++)
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{
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for(int j=0;j<N;j++)
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{
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printf("%.1f ",denseMatrixA[i][j]);
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}
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printf("\n");
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}
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printf("B的常规矩阵为：\n");
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for(int i=0;i<N;i++)
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{
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for(int j=0;j<Q;j++)
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{
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printf("%.1f ",denseMatrixB[i][j]);
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}
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printf("\n");
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}
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//为了之后的运用NEON来相乘，需要对B进行转置
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transposeMatrix(denseMatrixB,transposed) ;
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clock_t start = clock();
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matmul_optimized(denseMatrixA,transposed,C);
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clock_t end = clock();
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// 计算并输出优化的稀疏矩阵乘法的时间
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double time_spent = double(end - start) / CLOCKS_PER_SEC;
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printf("当矩阵A的维度为%d*%d,矩阵B的维度为%d*%d时，优化的稀疏矩阵乘法的用时：%lf秒\n", M,N,N,Q,time_spent);
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// 释放动态分配的内存
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for (int i = 0; i < M; ++i) {
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free(denseMatrixA[i]);free(C[i]);
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}
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for (int i = 0; i < N ;++i) {
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free(denseMatrixB[i]);
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}
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for (int i = 0; i < Q ;++i) {
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free(transposed[i]);
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}
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free(denseMatrixA); free(denseMatrixB); free(C);free(transposed);
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} |
