Add task7.cpp

task7.cpp

@@ -0,0 +1,179 @@
+#include <stdio.h>
+#include <ctime>
+#include <stdlib.h>
+#include <arm_neon.h> //启用 NEON 指令
+
+#define SIZE 102400000
+
+// 基础向量加法函数
+void vector_add(float* A, float* B, float* C, int size) 
+{
+    for (int i = 0; i < size; ++i) 
+	{
+        C[i] = A[i] + B[i];
+    }
+}
+//优化的向量加法函数
+void vector_add_optimized(float* A, float* B, float* C, int size)
+{
+	for(int i=0;i<SIZE;i+=4)
+	{
+		//加载A和B向量的4个浮点数到NEO寄存器 
+		float32x4_t vecA=vld1q_f32(&A[i]);
+		float32x4_t vecB=vld1q_f32(&B[i]);
+        //执行向量加法 
+        float32x4_t vecC=vaddq_f32(vecA,vecB);
+        //将结果存储到C向量 
+        vst1q_f32(&C[i],vecC);
+	}
+}
+
+//基础的矩阵乘法
+#define M  2000
+#define N  2000
+#define Q  2000
+//A(M*N),B(N*Q),C(M*Q)对于矩阵维度的说明
+void matmul(float** A, float** B, float** C)
+{
+	for (int i = 0; i < M; ++i) 
+	{	
+		for (int j = 0; j < Q; ++j) 
+		{
+			C[i][j] = 0;
+			for (int k = 0; k < N; ++k) 
+			{	
+				C[i][j] += A[i][k] * B[k][j];
+			}
+		}
+	}
+}
+
+//转置函数
+void transposeMatrix(float** matrix, float** transposed) {
+    for (int i = 0; i < N; i++) {
+        for (int j = 0; j < Q; j++) {
+            transposed[j][i] = matrix[i][j];
+        }
+    }
+}
+//优化的乘法函数
+void matmul_optimized(float** A, float** B, float** C) {
+    for (int i = 0; i < M; i++) {
+        for (int j = 0; j < Q; j++) {
+            float sum = 0.0f;
+            for (int k = 0; k < N; k += 4) 
+            {
+                float32x4_t vecA, vecB, vecC;
+                if (k + 4 <= N) 
+                {
+                    // 加载A和B的4个元素，进行向量化计算
+                    vecA = vld1q_f32(&A[i][k]);
+                    vecB = vld1q_f32(&B[j][k]);
+                    // 向量化乘法并累加结果
+                    vecC = vmulq_f32(vecA, vecB);
+                    sum += vgetq_lane_f32(vecC, 0) + vgetq_lane_f32(vecC, 1) +
+                           vgetq_lane_f32(vecC, 2) + vgetq_lane_f32(vecC, 3);
+                } 
+                else 
+                {
+                    // 处理剩余的元素
+                    for (int m = k; m < N; m++) 
+                    {
+                        sum += A[i][m] * B[j][m];
+                    }
+                }
+            }
+            C[i][j] = sum;
+        }
+    }
+}
+int main() 
+{
+    srand(time(NULL));
+
+    // 动态分配内存，并检查是否分配成功
+    float *A = (float *)malloc(SIZE * sizeof(float));
+    float *B = (float *)malloc(SIZE * sizeof(float));
+    float *C = (float *)malloc(SIZE * sizeof(float));
+    // 随机初始化，生成0到99之间的随机浮点数
+    for (int i = 0; i < SIZE; i++) 
+	{
+        A[i] = (float)(rand() % 100) / 100.0f;
+        B[i] = (float)(rand() % 100) / 100.0f;
+    }
+
+    printf("当SIZE取%d时\n", SIZE);
+    clock_t start1 = clock();
+    vector_add(A, B, C, SIZE);
+    clock_t end1 = clock();
+    // 计算并输出向量加法的时间
+    double basical_time_spent = double(end1 - start1) / CLOCKS_PER_SEC;
+    printf("使用基础的向量加法用时%lf秒\n", basical_time_spent);
+
+    clock_t start2 = clock();
+    vector_add_optimized(A, B, C, SIZE);
+    clock_t end2 = clock();
+    // 计算并输出向量加法的时间
+    double time_spent = double(end2 - start2) / CLOCKS_PER_SEC;
+    printf("使用NEON 优化向量加法用时%lf秒\n", time_spent);
+    // 释放动态分配的内存
+    free(A);free(B);free(C);
+
+    // 分配矩阵内存
+    float** A2 = (float**)malloc(M * sizeof(float*));
+    float** B2 = (float**)malloc(N * sizeof(float*));
+    float** C2 = (float**)malloc(M * sizeof(float*));
+    for (int i = 0; i < M; ++i) 
+    {
+        A2[i] = (float*)malloc(N * sizeof(float));
+        C2[i] = (float*)malloc(Q * sizeof(float));
+    }
+    for (int i = 0; i < N; ++i) 
+    {
+        B2[i] = (float*)malloc(Q * sizeof(float));
+    }
+    // 初始化矩阵数据
+    for (int i = 0; i < M; i++) {
+        for (int j = 0; j < N; j++) {
+            A2[i][j] = (float)(rand() % 100) / 100.0f;
+        }
+    }
+    for (int i = 0; i < N; i++) {
+        for (int j = 0; j < Q; j++) {
+            B2[i][j] = (float)(rand() % 100) / 100.0f;
+        }
+    }
+
+    printf("\n");
+    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);
+    //计算基础用时
+    clock_t start3 = clock();
+    matmul(A2, B2, C2);
+    clock_t end3 = clock();
+    double multiply_time_spent = double(end3 - start3) / CLOCKS_PER_SEC;
+    printf("使用基础的矩阵乘法用时：%lf秒\n",multiply_time_spent);
+    //计算 优化用时
+    float** transposed = (float**)malloc(Q * sizeof(float*));
+    for (int i = 0; i < Q; ++i) 
+    {
+        transposed[i] = (float*)malloc(N * sizeof(float));
+    }
+    transposeMatrix(B2, transposed);
+    clock_t start4 = clock();
+    matmul_optimized(A2, transposed, C2);
+    clock_t end4 = clock();
+    double optimized_multiply_time_spent = double(end4 - start4) / CLOCKS_PER_SEC;
+    printf("使用优化的矩阵乘法用时：%lf秒\n", optimized_multiply_time_spent);
+
+    // 释放动态分配的内存
+    for (int i = 0; i < M; ++i) {
+        free(A2[i]);free(C2[i]);
+    }
+    for (int i = 0; i < N ;++i) {
+        free(B2[i]);
+    }
+    for (int i = 0; i < Q ;++i) {
+        free(transposed[i]);
+    }
+    free(A2); free(B2); free(C2);free(transposed);
+}