[Beowulf] Nehalem and Shanghai code performance for our rzf example

Kevin Abbey kabbey at biomaps.rutgers.edu
Sat Jan 17 13:14:12 EST 2009


Hi Joe,
Can that 9% difference be due to the Intel capability to overclock one 
core and turn the others off?
Or is does this Intel feature require manual switch somewhere?
Thank  you,
Kevin


Joe Landman wrote:
> Hi folks:
>
>   Thought you might like to see this.  I rewrote the interior loop for 
> our Riemann Zeta Function (rzf) example for SSE2, and ran it on a 
> Nehalem and on a Shanghai.  This code is compute intensive.  The inner 
> loop which had been written as this (some small hand optimization, 
> loop unrolling, etc):
>
>     l[0]=(double)(inf-1 - 0);
>     l[1]=(double)(inf-1 - 1);
>     l[2]=(double)(inf-1 - 2);
>     l[3]=(double)(inf-1 - 3);
>     p_sum[0] = p_sum[1] = p_sum[2] = p_sum[3] = zero;
>     for(k=start_index;k>end_index;k-=unroll)
>        {
>           d_pow[0] = l[0];
>           d_pow[1] = l[1];
>           d_pow[2] = l[2];
>           d_pow[3] = l[3];
>
>           for (m=n;m>1;m--)
>            {
>              d_pow[0] *=  l[0];
>              d_pow[1] *=  l[1];
>              d_pow[2] *=  l[2];
>              d_pow[3] *=  l[3];
>            }
>           p_sum[0] += one/d_pow[0];
>           p_sum[1] += one/d_pow[1];
>           p_sum[2] += one/d_pow[2];
>           p_sum[3] += one/d_pow[3];
>
>           l[0]-=four;
>           l[1]-=four;
>           l[2]-=four;
>           l[3]-=four;
>       }
>     sum = p_sum[0] + p_sum[1] + p_sum[2] + p_sum[3] ;
>
> has been rewritten as
>
>     __m128d __P_SUM = _mm_set_pd1(0.0);        // __P_SUM[0 ... VLEN] = 0
>     __m128d __ONE = _mm_set_pd1(1.);   // __ONE[0 ... VLEN] = 1
>     __m128d __DEC = _mm_set_pd1((double)VLEN);
>     __m128d __L   = _mm_load_pd(l);
>
>     for(k=start_index;k>end_index;k-=unroll)
>        {
>           __D_POW       = __L;
>
>           for (m=n;m>1;m--)
>            {
>              __D_POW    = _mm_mul_pd(__D_POW, __L);
>            }
>
>           __P_SUM       = _mm_add_pd(__P_SUM, _mm_div_pd(__ONE, 
> __D_POW));
>
>           __L           = _mm_sub_pd(__L, __DEC);
>
>       }
>
>     _mm_store_pd(p_sum,__P_SUM);
>
>     for(k=0;k<VLEN;k++)
>      {
>        sum += p_sum[k];
>      }
>
> The two codes were run on a Nehalem 3.2 GHz (desktop) processor, and a 
> Shanghai 2.3 GHz desktop processor.  Here are the results
>
>     Code        CPU    Freq (GHz)    Wall clock (s)
>     ------        -------    -------------    --------------
>
>     base        Nehalem    3.2        20.5       
>     optimized    Nehalem    3.2        6.72       
>     SSE-ized    Nehalem    3.2        3.37
>
>     base        Shanghai 2.3        30.3
>     optimized    Shanghai 2.3        7.36        
>     SSE-ized    Shanghai 2.3        3.68
>     
> These are single thread, single core runs.  Code scales very well (is 
> one of our example codes for the HPC/programming/parallelization 
> classes we do).
>
> I found it interesting that they started out with the baseline code 
> performance tracking the ratio of clock speeds ... The Nehalem has a 
> 39% faster clock, and showed 48% faster performance, which is about 9% 
> more than could be accounted for by clock speed alone.  The SSE code 
> performance appears to be about 9% different.
>
> I am sure lots of interesting points can be made out of this (being 
> only one test, and not the most typical test/use case either, such 
> points may be of dubious value).
>
> I am working on a Cuda version of the above as well, and will try to 
> compare this to the threaded versions of the above.  I am curious what 
> we can achieve.
>
> Joe
>

-- 
Kevin C. Abbey
System Administrator
Rutgers University - BioMaPS Institute

Email: kabbey at biomaps.rutgers.edu


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