Maximum path sum I Problem 18

本文探讨了C++中二维数组的优化方法,通过实现数据结构的局部优化来提升性能。详细介绍了如何使用二维数组进行数据存储与操作,并提供了一种改进的数据结构策略以提高效率。

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#include<iostream>
using namespace std;
int main()
{
int data[15][15] = {
        {75},
        {95,64},
        {17,47,82},
        {18,35,87,10},
        {20,4,82,47,65},
        {19,1,23,75,3,34},
        {88,2,77,73,7,63,67},
        {99,65,4,28,6,16,70,92},
        {41,41,26,56,83,40,80,70,33},
        {41,48,72,33,47,32,37,16,94,29},
        {53,71,44,65,25,43,91,52,97,51,14},
        {70,11,33,28,77,73,17,78,39,68,17,57},
        {91,71,52,38,17,14,91,43,58,50,27,29,48},
        {63,66,4,68,89,53,67,30,73,16,69,87,40,31},
        {4,62,98,27,23,9,70,98,73,93,38,53,60,4,23,}};

    for(int i=14;i>=0;i--)  
    {  
        n--;  
        for (int j=0;j<n;j++)  
            if (data[i][j]>data[i][j+1])  
                data[i-1][j]+=data[i][j];  
            else  
                data[i-1][j]+=data[i][j+1];  
  
    }  
cout<<data[0][0]<<endl;
}
优化这段代码,窗口1、3、5、9、11、17、33,输入3200个点,输出也要3200个点,边缘输出原值:#include "SmoothProfileOnXAxisMean.h" #define MAX_WINDOW_SIZE 33 #define HALF_MAX_WINDOW 16 #define II 1 // 每周期处理一个数据 // 使用18位定点数(16位整数 + 8位小数)替代浮点数 typedef ap_fixed<24, 12> fixed_t; typedef ap_fixed<32, 16> fixed_cache; void SmoothProfileOnXAxisMeanOptimize(hls::stream<float>& points_in_z, hls::stream<float>& smoothed_z, ap_uint<6> mean_win_size, float invalid_z ) { #pragma HLS PIPELINE II=1 #pragma HLS INTERFACE ap_ctrl_none port=return #pragma HLS INTERFACE axis port=points_in_z #pragma HLS INTERFACE axis port=smoothed_z #pragma HLS INTERFACE ap_none port=mean_win_size #pragma HLS INTERFACE ap_none port=invalid_z // 循环缓冲区 - 使用定点数提高时序性能 static fixed_t buffer[MAX_WINDOW_SIZE]; #pragma HLS ARRAY_PARTITION variable=buffer complete dim=1 static ap_uint<1> win[MAX_WINDOW_SIZE]; #pragma HLS ARRAY_PARTITION variable=win complete dim=1 static ap_uint<1> invalid_flag0; static ap_uint<1> invalid_flag1; static ap_uint<1> invalid_flag2; // 读取输入并转换为定点数 float in_val_float = points_in_z.read(); fixed_t in_val = in_val_float; fixed_t invalid_z_fixed = invalid_z; // std::cout << "out1_data:" << in_val << " " << invalid_z_fixed << " " << invalid_z <<std::endl; for (int i = MAX_WINDOW_SIZE-1; i > 0; i--){ #pragma HLS UNROLL buffer[i] = buffer[i - 1]; win[i] = win[i-1]; } buffer[0] = (in_val==invalid_z_fixed)?(fixed_t)0:in_val; win[0] = (in_val==invalid_z_fixed)?0:1; static fixed_cache sum_buffer0[11]; static fixed_cache sum_win0[11]; for (int i = 0; i < 11; i=i+1) { #pragma HLS UNROLL sum_buffer0[i] = buffer[i * 3] + buffer[i * 3+1] + buffer[i * 3+2]; sum_win0[i] = win[i * 3] + win[i * 3+1] + win[i * 3+2]; } invalid_flag0 = win[16]; static fixed_cache sum_buffer1[3]; sum_buffer1[0] = sum_buffer0[0] + sum_buffer0[1] + sum_buffer0[2] + sum_buffer0[3]; sum_buffer1[1] = sum_buffer0[4] + sum_buffer0[5] + sum_buffer0[6] + sum_buffer0[7]; sum_buffer1[2] = sum_buffer0[8] + sum_buffer0[9] + sum_buffer0[10]; static fixed_cache sum_win1[3]; sum_win1[0] = sum_win0[0] + sum_win0[1] + sum_win0[2] + sum_win0[3]; sum_win1[1] = sum_win0[4] + sum_win0[5] + sum_win0[6] + sum_win0[7]; sum_win1[2] = sum_win0[8] + sum_win0[9] + sum_win0[10]; invalid_flag1 = invalid_flag0; static fixed_cache sum_buffer2; sum_buffer2 = sum_buffer1[0] + sum_buffer1[1] + sum_buffer1[2]; static fixed_cache sum_win2; sum_win2 = sum_win1[0] + sum_win1[1] + sum_win1[2]; invalid_flag2 = invalid_flag1; // std::cout << "out2_data:" << in_val << " " << sum_buffer2 << " " << sum_win2 <<std::endl; // 除法优化 - 使用移位和乘法避免硬件除法器 fixed_t result; if (invalid_flag2 == 0) { result = invalid_z_fixed; } else { // 使用倒数乘法代替除法 ap_ufixed<24, 8> reciprocal = 1.0 / sum_win2.to_int(); result = fixed_t(sum_buffer2 * reciprocal); } // std::cout << "out2_data:" << in_val << " " << result << " " << sum_buffer2 << " " << sum_win2 << " " << std::endl; smoothed_z.write((float)result); }
07-29
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