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算子优化系统4.cpp

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+#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;
+}