# Why worse performace in euclidean distance with SSE2?

Dario Bahena Tapia dario.mx@gmail.com
Tue Apr 8 15:57:00 GMT 2008

```Hello,

Yeah, others have suggested as well changing the way i process them in
order to allow for that. Working there ;-|

Will consider the other suggestions as well !!!

Thanks.

On Tue, Apr 8, 2008 at 2:07 AM, Zuxy Meng <zuxy.meng@gmail.com> wrote:
> Hi,
>
>  "Dario Bahena Tapia" <dario.mx@gmail.com>
> 写入消息新闻:3d104d6f0804070617u47213cc8nbc697dab9dc262b5@mail.gmail.com...
>
>
>
> > Hello,
> >
> > I have just begun to play with SSE2 and gcc intrinsics. Indeed, maybe
> > this question is not exactly about gcc  ... but I think gcc lists are
> > a very good place to find help from  hardcore assembler hackers ;-1
> >
> > I have a program which makes heavy usage of euclidean distance
> > function. The serial version is:
> >
> > inline static double dist(int i,int j)
> > {
> >  double xd = C[i][X] - C[j][X];
> >  double yd = C[i][Y] - C[j][Y];
> >  return rint(sqrt(xd*xd + yd*yd));
> > }
> >
> > As you can see each C[i] is an array of two double which represents a
> > 2D vector (indexes 0 and 1 are coordinates X,Y respectively). I tried
> > to vectorize the function using SSE2 and gcc intrinsics, here is the
> > result:
> >
> > inline static double dist_sse(int i,int j)
> > {
> >  double d;
> >  __m128d xmm0,xmm1;
> >  xmm0 = _mm_sub_pd(xmm0,xmm1);
> >  xmm1 = xmm0;
> >  xmm0 = _mm_mul_pd(xmm0,xmm1);
> >  xmm1 = _mm_shuffle_pd(xmm0, xmm0, _MM_SHUFFLE2(1, 1));
> >  xmm0 = _mm_sqrt_pd(xmm0);
> >  _mm_store_sd(&d,xmm0);
> >  return rint(d);
> > }
> >
> > Of course each C[i] was aligned as SSE2 expects:
> >
> > for(i=0; i<D; i++)
> > C[i] = (double *) _mm_malloc(2 * sizeof(double), 16);
> >
> > And in order to activate the SSE2 features, I am using the following
> > flags for gcc (my computer is a laptop):
> >
> > CFLAGS = -O -Wall -march=pentium-m -msse2
> >
> > The vectorized version of the function seems to be correct, given it
> > provides same results as serial counterpart. However, the performace
> > is poor; execution time of program increases in approximately 50% (for
> > example, in calculating the distance of every pair of points from a
> > set of 10,000, the serial version takes around 8 seconds while
> > vectorized flavor takes 12).
> >
> > I was expecting a better time given that:
> >
> > 1. The difference of X and Y is done in parallel
> > 2. The product of each difference coordinate with itself is also done
> > in parallel
> > 3. The sqrt function used is hardware implemented (although serial
> > sqrt implementation could also take advantage of hardware)
> >
> > I suppose the problem here is my lack of experience programming in
> > assembler in general, and in particular with SSE2. Therefore, I am
> >
>
>  1. First of all, you didn't extract the parallelism in your algorithm. SSE2
> won't help you if all you want is to pick up two points at random indices
> and calculate the distance. However it will help you a lot when you
> calculate the distances between a given point and 1 million others whose
> indices are sequential.
>
>  2. Unroll the loop to hide the latency of square root as much as possible.
>
>  3. Since the final result is an integer, you may consider using "float"
> instead of "double". That'll give you a performance boost even without SSE2.
> And rsqrtps comes in handy too, if its precision is acceptable.
>
>  --
>  Zuxy
>
>
```