Profiling calls to small functions

[albert-j via Digitalmars-d-learn]

Let's say I want to create an array of random numbers and do some 
operations on them:

void main() {
    import std.random;

    //Generate array of random numbers
    int arrSize = 100000000;
    double[] arr = new double[](arrSize);
    foreach (i; 0..arrSize)
        arr[i] = uniform01();

    //Call funcA on array elements
    foreach (i; 1..arr.length-1)
        funcA(arr,i);
}

void funcA(double[] arr, size_t i) {
    arr[i+1] = arr[i-1]+arr[i];
    funcB(arr,i);
}

void funcB(double[] arr, size_t i) {
    arr[i-1]= arr[i] + arr[i+1];
    arr[i] = arr[i-1] + arr[i+1];
    arr[i+1]= arr[i-1] + arr[i];
}

Now I dmd -profile it and look at the performance of funcA with 
d-profile-viewer. Inside funcA, only 20% of time is spend in 
funcB, but the rest 80% is self-time of funcA. How is it 
possible, when funcB has three times the calculations of funcA? 
It appears that the call to funcB itself is very expensive.