Posted by
Kim Kuen Tang on
URL: http://quantlib.414.s1.nabble.com/MonteCarlo-Simulation-tp8137p8138.html
Hi Deepak,
there are several ways to improve the performance. Before you do that i'
ll suggest you to use boost::chrono to see where the bottleneck of your
application
is.
http://svn.boost.org/svn/boost/sandbox/chrono/libs/chrono/doc/html/index.htmlIt will measure the time you use for your application. For example.
real 0.832s, cpu 0.813s (97.7%), user 0.813s, system 0.000s
After that you can playing around by changing the container ( from
vector to list or to quantlib::array, boost::array,...)
Try to rewrite your payoff function.
If all this dont help , i 'll suggest you to port your calculation into
a graphic processor. To help you get starting see the article from
Mark S. Joshi
Graphical Asian Options
Abstract
We study the problem of pricing an Asian option using CUDA on a graphics
processing unit. We demonstrate that it is possible to get accuracy of
2E-4 in less than a fiftieth of a second.
Cheers,
Kim
Deepak schrieb:
>
> Hi Guys,
>
> I’m using *Monte Carlo engine and Path Generators* to price some
> option types, I want to know what steps should I take to reduce the
> computation time, or I can say I want to improve performance, I know
> it all depends on the number of iterations, but I can’t reduce it, I
> want other options like distributed/Parallel/Multithreading or
> anything similar, to price my options, if there is anything already
> done in this direction please tell me,
>
> I want you guys to give your thoughts even if I’ll have to implement,
> I want to discuss first, QuantLib is a great library may be I can
> contribute, Please give your inputs.
>
> Thanks & Regards,
> *Deepak*
>
>
>
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