如何在不使用并行化或SSE的前提下优化C++分箱统计循环

我正尝试在不使用并行化或SSE技术的前提下优化一段C++代码。当前在O2优化级别下，关键代码段在我的PC上运行耗时约20ms，即便对于约1700万次迭代来说，这个耗时也偏长。较慢的核心代码段如下：

for (int d = 0; d < numDims; d++)
{
    for (int i = 0; i < numNodes; i++)
    {
        bins[d][(int) (floodVals[d][i] * binSteps)]++;
    }
}

更新：改用迭代器后运行时间降至17ms，代码如下：

for (int d = 0; d < numDims; d++)
{
    std::vector<float>::iterator floodIt;
    for (floodIt = floodVals[d].begin(); floodIt < floodVals[d].end(); floodIt++)
    {
        bins[d][(int) (*floodIt * binSteps)]++;
    }
}

完整测试代码如下：

#include <vector>
#include <random>
#include <iostream>
#include <chrono>

int main()
{
    // Initialize random normalized input [0, 1)
    std::random_device rd;
    std::mt19937 gen(rd());
    std::uniform_real_distribution<float> dist(0, 0.99999);

    // Initialize dimensions
    const int numDims = 130;
    const int numNodes = 130000;
    const int binSteps = 30;

    // Make dummy data
    std::vector<std::vector<float>> floodVals(numDims, std::vector<float>(numNodes));

    for (int d = 0; d < numDims; d++)
    {
        for (int i = 0; i < numNodes; i++)
        {
            floodVals[d][i] = dist(gen);
        }
    }

    // Initialize binning
    std::vector<std::vector<int>> bins(numDims, std::vector<int>(binSteps, 0));

    // Time critical section of code
    auto start = std::chrono::high_resolution_clock::now();

    for (int d = 0; d < numDims; d++)
    {
        for (int i = 0; i < numNodes; i++)
        {
            bins[d][(int) (floodVals[d][i] * binSteps)]++;
        }
    }

    auto finish = std::chrono::high_resolution_clock::now();
    std::chrono::duration<double> elapsed = finish - start;
    std::cout << "Elapsed: " << elapsed.count() * 1000 << " ms" << std::endl;

    return 0;
}

内容的提问来源于stack exchange，提问作者Taylee