[Scilab-users] About performance

[Amanda Osvaldo]

Thanks Clementé, this will change the way of how i work. :-D 
-- Amanda Osvaldo
On Mon, 2017-03-06 at 10:16 +0100, Clément David wrote:
> Hi Amanda,
> 
> Le jeudi 02 mars 2017 à 18:24 -0300, Amanda Osvaldo a écrit :
> > Just a note about performance in computing.
> > 
> > I read the Intel articles in Intel Developer Zone and frequently
> > they call to think about the
> > limitations in the memory cache and the set of instructions used to
> > do the computation.
> > 
> > Even if all the data can be vectorized, it's possible to put all
> > the data and code in the
> > processor cache to avoid them to search it in RAM?
> > 
> > I'm really like to take more control of it in my projects.
> > Is about computer engineering I don't know how to handle it.
> > 
> > https://software.intel.com/pt-br/articles/how-memory-is-accessed?ut
> > m_campaign=Servers-Brazil&utm_s
> > ource=Facebook&utm_medium=SocialMedia&utm_content=content_460
> 
> I don't know how to do on windows but on linux (using the perf tool)
> I can detect L1 and L2 cache
> miss for a running program. Starting from that I can easily reduce
> the data size until I got no more
> cache miss.
> 
> In a "simple way" using Scilab, you can try to split your problem
> accordingly to the memory cache of
> your machine. L1 cache might be used by the Scilab interpreter and
> simply use L2 cache size for data
> manipulation, in my case :
> 
> L1: 128 kB
> L2: 512 kB
> L3: 3072 kB
> 
> L1 is for Scilab interpreter and MKL speedup
> L2 can contain up to 65536 (512*1024/8) double values without
> performance penalty
> 
> So, I should split my problem to compute at most 65536 values to take
> the full advantage of L2. And
> after that, I should use a dedicated tool (linux perf for exemple) to
> discover what can be done
> better.
> 
> Regards,
> 
> --
> Clément
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