From 387e354d5b28e90b1bd241cd2e9bbd04cf89a95e Mon Sep 17 00:00:00 2001
From: pv3e4i5aj <lanjinzi@qq.com>
Date: Sat, 23 Nov 2024 18:10:44 +0800
Subject: [PATCH] Add task6.cpp

---
 task6.cpp | 153 ++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 153 insertions(+)
 create mode 100644 task6.cpp

diff --git a/task6.cpp b/task6.cpp
new file mode 100644
index 0000000..9d4de34
--- /dev/null
+++ b/task6.cpp
@@ -0,0 +1,153 @@
+#include <stdio.h>
+#include <arm_neon.h>
+#include <ctime>
+#include <stdlib.h>
+
+#define M  27
+#define N  25
+#define Q  12
+//A(M*N),B(N*Q),C(M*Q),transposed(Q*N)对于矩阵维度的说明
+
+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];
+        }
+    }
+}
+
+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()
+{	
+	//矩阵 A 的 COO 格式
+    float A_values[] = {1, 2, 3,4,5};
+    int A_rowIndex[] = {0, 0, 1, 2, 2};
+    int A_colIndex[] = {0, 2, 1,0, 2};
+    int A_nonZeroCount = 5;
+    // 矩阵 B 的 COO 格式
+    float B_values[] = {6,8,7,9};
+    int B_rowIndex[] = {0,2, 1, 2};
+    int B_colIndex[] ={0,0,1, 2};
+    int B_nonZeroCount=4; 
+
+
+	//动态分配内存
+    float** denseMatrixA = (float**)malloc(M * sizeof(float*));
+    float** denseMatrixB = (float**)malloc(N * sizeof(float*));
+    float** C = (float**)malloc(M * sizeof(float*));
+    float** transposed = (float**)malloc(Q * sizeof(float*));
+
+    for (int i = 0; i < M; i++) {
+        denseMatrixA[i] = (float*)malloc(N * sizeof(float));
+        C[i] = (float*)malloc(Q * sizeof(float));
+    }
+    for (int i = 0; i < N; ++i) 
+    {
+        denseMatrixB[i] = (float*)malloc(Q * sizeof(float));
+    }
+    for (int i = 0; i < Q; ++i) 
+    {
+        transposed[i] = (float*)malloc(N * sizeof(float));
+    }
+
+	// 	实现稀疏矩阵转换为普通矩阵
+    for(int i=0;i<M;i++)
+    {
+        for(int j=0;j<N;j++)
+        {
+            denseMatrixA[i][j]=0;
+        }
+    }
+
+    for(int i=0;i<N;i++)
+    {
+        for(int j=0;j<Q;j++)
+        {
+            denseMatrixB[i][j]=0;
+        }
+    }
+	for(int i = 0; i < A_nonZeroCount; i++) 
+	{
+		int row = A_rowIndex[i];
+		int col = A_colIndex[i];
+		denseMatrixA[row][col] = A_values[i];
+	}
+    	for(int i = 0; i < B_nonZeroCount; i++) 
+	{
+		int row = B_rowIndex[i];
+		int col = B_colIndex[i];
+		denseMatrixB[row][col] = B_values[i];
+	}
+	 
+     //打印常规矩阵
+     printf("A的常规矩阵为：\n");
+    for(int i=0;i<M;i++)
+    {
+        for(int j=0;j<N;j++)
+        {
+            printf("%.1f ",denseMatrixA[i][j]);
+        }
+        printf("\n");
+    }
+     printf("B的常规矩阵为：\n");
+    for(int i=0;i<N;i++)
+    {
+        for(int j=0;j<Q;j++)
+        {
+            printf("%.1f ",denseMatrixB[i][j]);
+        }
+        printf("\n");
+    }
+
+    //为了之后的运用NEON来相乘，需要对B进行转置
+    transposeMatrix(denseMatrixB,transposed) ;
+
+	clock_t start = clock();
+    matmul_optimized(denseMatrixA,transposed,C);
+    clock_t end = clock();
+
+    // 计算并输出优化的稀疏矩阵乘法的时间
+    double time_spent = double(end - start) / CLOCKS_PER_SEC;
+    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);
+
+    // 释放动态分配的内存
+    for (int i = 0; i < M; ++i) {
+        free(denseMatrixA[i]);free(C[i]);
+    }
+    for (int i = 0; i < N ;++i) {
+        free(denseMatrixB[i]);
+    }
+    for (int i = 0; i < Q ;++i) {
+        free(transposed[i]);
+    }
+    free(denseMatrixA); free(denseMatrixB); free(C);free(transposed);
+}
\ No newline at end of file