Re: An example of poor C++ performance

[Todd Neal]

Steve C. Thompson wrote:
> Hello Group,
>
> My previous posts have been leading up to this.  I have successfully 
> converted my m-file simulation code to C++, which can then be loaded
> as an .oct module.  To test speed improvements, I've made a simple
> example comparing the simulation with two approaches:
>
> 1) pure m-file (vectorized) 2) m-file/C++ hybrid
>
> Each approach generates about 1e9 random numbers.  The pure m-file is
>  vectorized by crunching numbers in 1e4 blocks (which I've found to
> be optimum on my machine).  However, over 50,000 loops are required.
> The hybrid m-file/C++ diverts the looping C++.

I may be wrong, but I think that the problem is that the comparison is
not quite valid.  randn() is called as randn(1,v) , with v being 10000.
 This appears to call octave_rand::nd_array which runs in a tight loop
calling a Fortran function and filling the array.


> This approach only has 46 m-file loops and the rest is done in the
> compiled module.  For each C++ loop a couple random number are
> generated with the command octave_rand::scalar().
>
> I was expecting C++ implementation to outperform the pure m-file 
> approach, but this isn't the case.

The C++ version doesn't take advantage of octave_rand::nd_array and
instead calls octave_rand::scalar which as I detailed in an earlier
email calls maybe_initialize() which checks a flag, and then calls the
Fortran function.  You get many more function calls and returns this way
including redundant calls to maybe_initialize().

I haven't tested this theory but if you wrote the m-file program in the
same way, calling rand(1) for each pseudo-random number needed, it would
probably take much more time than the C++ program.

Todd



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