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main
ptslgaequ 11 hours ago
parent 9125d5c2f9
commit 8bc939f625

@ -0,0 +1,95 @@
#include <stdio.h>
#include <stdlib.h>
#include <ctime>
// 包含NEON头文件启用NEON指令
#include <arm_neon.h>
// 定义矩阵大小
#define SIZE 1024
// 基础的矩阵乘法函数
void matmul(float** A, float** B, float** C, int n) {
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
float sum = 0;
for (int k = 0; k < n; k++) {
sum += A[i][k] * B[k][j];
}
C[i][j] = sum;
}
}
}
// 使用NEON指令优化的矩阵乘法函数
void matmul_optimized(float** A, float** B, float** C, int n) {
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
float32x4_t vecC = vdupq_n_f32(0); // 初始化结果向量为0
for (int k = 0; k < n; k += 4) {
// 向量加载将A和B的4个连续元素加载到float32x4_t类型的向量中
float32x4_t a_vec = vld1q_f32(&A[i][k]);
float32x4_t b_vec = vld1q_f32(&B[k][j]);
// 向量乘法和累加使用vmlaq_f32完成对应元素相乘并累加到vecC中
vecC = vmlaq_f32(vecC, a_vec, b_vec);
}
// 向量还原提取累加结果并存储到C[i][j]中
C[i][j] = vgetq_lane_f32(vecC, 0) + vgetq_lane_f32(vecC, 1) +
vgetq_lane_f32(vecC, 2) + vgetq_lane_f32(vecC, 3);
}
}
}
int main() {
// 动态分配两个输入矩阵A和B以及结果矩阵C的内存
float** A = (float**)malloc(SIZE * sizeof(float*));
for (int i = 0; i < SIZE; i++) {
A[i] = (float*)malloc(SIZE * sizeof(float));
}
float** B = (float**)malloc(SIZE * sizeof(float*));
for (int i = 0; i < SIZE; i++) {
B[i] = (float*)malloc(SIZE * sizeof(float));
}
float** C = (float**)malloc(SIZE * sizeof(float*));
for (int i = 0; i < SIZE; i++) {
C[i] = (float*)malloc(SIZE * sizeof(float));
}
float** C_optimized = (float**)malloc(SIZE * sizeof(float*));
for (int i = 0; i < SIZE; i++) {
C_optimized[i] = (float*)malloc(SIZE * sizeof(float));
}
// 初始化矩阵数据将A和B矩阵的每个元素随机初始化
for (int i = 0; i < SIZE; i++) {
for (int j = 0; j < SIZE; j++) {
A[i][j] = (float)(rand() % 100);
B[i][j] = (float)(rand() % 100);
}
}
// 测试基础矩阵乘法函数的运行时间
clock_t start_time_original = clock();
matmul(A, B, C, SIZE);
clock_t end_time_original = clock();
double elapsed_time_original = (double)(end_time_original - start_time_original) / CLOCKS_PER_SEC;
// 测试NEON优化后的矩阵乘法函数的运行时间
clock_t start_time_optimized = clock();
matmul_optimized(A, B, C_optimized, SIZE);
clock_t end_time_optimized = clock();
double elapsed_time_optimized = (double)(end_time_optimized - start_time_optimized) / CLOCKS_PER_SEC;
// 输出基础矩阵乘法的运行时间
printf("original time: %lf s\n", elapsed_time_original);
// 输出NEON优化后的矩阵乘法的运行时间
printf("NEON optimized time: %lf s\n", elapsed_time_optimized);
// 释放动态分配的内存空间
for (int i = 0; i < SIZE; i++) {
free(A[i]);
}
free(A);
for (int i = 0; i < SIZE; i++) {
free(B[i]);
}
free(B);
for (int i = 0; i < SIZE; i++) {
free(C[i]);
}
free(C);
for (int i = 0; i < SIZE; i++) {
free(C_optimized[i]);
}
free(C_optimized);
return 0;
}
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