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#include <stdio.h>
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#include <ctime>
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#include <stdlib.h>
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#include <arm_neon.h> //启用 NEON 指令
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#define SIZE 102400000
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// 基础向量加法函数
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void vector_add(float* A, float* B, float* C, int size)
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{
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for (int i = 0; i < size; ++i)
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{
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C[i] = A[i] + B[i];
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}
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}
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//优化的向量加法函数
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void vector_add_optimized(float* A, float* B, float* C, int size)
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{
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for(int i=0;i<SIZE;i+=4)
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{
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//加载A和B向量的4个浮点数到NEO寄存器
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float32x4_t vecA=vld1q_f32(&A[i]);
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float32x4_t vecB=vld1q_f32(&B[i]);
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//执行向量加法
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float32x4_t vecC=vaddq_f32(vecA,vecB);
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//将结果存储到C向量
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vst1q_f32(&C[i],vecC);
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}
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}
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//基础的矩阵乘法
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#define M 2000
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#define N 2000
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#define Q 2000
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//A(M*N),B(N*Q),C(M*Q)对于矩阵维度的说明
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void matmul(float** A, float** B, float** C)
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{
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for (int i = 0; i < M; ++i)
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{
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for (int j = 0; j < Q; ++j)
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{
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C[i][j] = 0;
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for (int k = 0; k < N; ++k)
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{
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C[i][j] += A[i][k] * B[k][j];
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}
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}
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}
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}
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//转置函数
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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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//优化的乘法函数
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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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srand(time(NULL));
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// 动态分配内存，并检查是否分配成功
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float *A = (float *)malloc(SIZE * sizeof(float));
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float *B = (float *)malloc(SIZE * sizeof(float));
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float *C = (float *)malloc(SIZE * sizeof(float));
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// 随机初始化，生成0到99之间的随机浮点数
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for (int i = 0; i < SIZE; i++)
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{
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A[i] = (float)(rand() % 100) / 100.0f;
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B[i] = (float)(rand() % 100) / 100.0f;
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}
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printf("当SIZE取%d时\n", SIZE);
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clock_t start1 = clock();
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vector_add(A, B, C, SIZE);
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clock_t end1 = clock();
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// 计算并输出向量加法的时间
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double basical_time_spent = double(end1 - start1) / CLOCKS_PER_SEC;
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printf("使用基础的向量加法用时%lf秒\n", basical_time_spent);
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clock_t start2 = clock();
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vector_add_optimized(A, B, C, SIZE);
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clock_t end2 = clock();
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// 计算并输出向量加法的时间
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double time_spent = double(end2 - start2) / CLOCKS_PER_SEC;
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printf("使用NEON 优化向量加法用时%lf秒\n", time_spent);
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// 释放动态分配的内存
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free(A);free(B);free(C);
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// 分配矩阵内存
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float** A2 = (float**)malloc(M * sizeof(float*));
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float** B2 = (float**)malloc(N * sizeof(float*));
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float** C2 = (float**)malloc(M * sizeof(float*));
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for (int i = 0; i < M; ++i)
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{
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A2[i] = (float*)malloc(N * sizeof(float));
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C2[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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B2[i] = (float*)malloc(Q * sizeof(float));
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}
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// 初始化矩阵数据
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for (int i = 0; i < M; i++) {
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for (int j = 0; j < N; j++) {
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A2[i][j] = (float)(rand() % 100) / 100.0f;
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}
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}
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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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B2[i][j] = (float)(rand() % 100) / 100.0f;
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}
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}
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printf("\n");
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printf("当矩阵A2的维度为%d*%d,矩阵B2的维度为%d*%d时\n",M,N,N,Q);
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//计算基础用时
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clock_t start3 = clock();
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matmul(A2, B2, C2);
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clock_t end3 = clock();
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double multiply_time_spent = double(end3 - start3) / CLOCKS_PER_SEC;
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printf("使用基础的矩阵乘法用时：%lf秒\n",multiply_time_spent);
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//计算 优化用时
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float** transposed = (float**)malloc(Q * sizeof(float*));
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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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transposeMatrix(B2, transposed);
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clock_t start4 = clock();
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matmul_optimized(A2, transposed, C2);
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clock_t end4 = clock();
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double optimized_multiply_time_spent = double(end4 - start4) / CLOCKS_PER_SEC;
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printf("使用优化的矩阵乘法用时：%lf秒\n", optimized_multiply_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(A2[i]);free(C2[i]);
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}
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for (int i = 0; i < N ;++i) {
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free(B2[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(A2); free(B2); free(C2);free(transposed);
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} |
