How to vectorize a distance calculation using SSE2

问题:

A and B are vectors or length N, where N could be in the range 20 to 200 say. I want to calculate the square of the distance between these vectors, i.e. d^2 = ||A-B||^2.

So far I have:

float* a = ...;
float* b = ...;
float d2 = 0;

for(int k = 0; k < N; ++k)
{
    float d = a[k] - b[k];
    d2 += d * d;
}

That seems to work fine, except that I have profiled my code and this is the bottleneck (more than 50% of time is spent just doing this). I am using Visual Studio 2012, on Win 7, with these optimization options: /O2 /Oi /Ot /Oy-. My understanding is that VS2012 should auto-vectorize that loop (using SSE2). However if I insert #pragma loop(no_vector) in the code I don't get a noticable slow down, so I guess the loop is not being vectorized. The compiler confirms that with this message:

  info C5002: loop not vectorized due to reason '1105'

My questions are:

1. Is it possible to fix this code so that VS2012 can vectorize it?
2. If not, would it make sense to try to vectorize the code myself?
3. Can you recommend a web site for me to learn about SSE2 coding?
4. Is there some value of N below which vectorization would be counter productive?
5. What is reason '1105'?

回答1:

From the MSDN documentation, the 1105 error code means the compiler is not able to figure out how to reduce the code to vectorized instructions. For floating point operations it is indicated that you need to specify the /fp:fast option to enable any floating point reductions at all.

回答2:

It's pretty straightforward to implement this using SSE intrinsics:

#include "pmmintrin.h"

__m128 vd2 = _mm_set1_ps(0.0f);
float d2 = 0.0f;
int k;

// process 4 elements per iteration
for (k = 0; k < N - 3; k += 4)
{
    __m128 va = _mm_loadu_ps(&a[k]);
    __m128 vb = _mm_loadu_ps(&b[k]);
    __m128 vd = _mm_sub_ps(va, vb);
    vd = _mm_mul_ps(vd, vd);
    vd2 = _mm_add_ps(vd2, vd);
}

// horizontal sum of 4 partial dot products
vd2 = _mm_hadd_ps(vd2, vd2);
vd2 = _mm_hadd_ps(vd2, vd2);
_mm_store_ss(&d2, vd2);

// clean up any remaining elements
for ( ; k < N; ++k)
{
    float d = a[k] - b[k];
    d2 += d * d;
}

Note that if you can guarantee that a and b are 16 byte aligned then you can use _mm_load_ps rather than _mm_loadu_ps which may help performance, particularly on older (pre Nehalem) CPUs.

Note also that for loops such as this where the are very few arithmetic instructions relative to the number of loads then performance may well be limited by memory bandwidth and the expected speed-up from vectorization may not be realised in practice.