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Nvidia 9600GT. Plenty of room to upgrade....
--- In amibroker@xxxxxxxxxxxxxxx, Keith McCombs <kmccombs@xxx> wrote:
>
> Just curious -- would you mind telling us exactly which board you
are using?
> Thanks.
>
> dloyer123 wrote:
> >
> > I was able to get a AmiBroker dll to work with Nvidia CUDA
drivers.
> >
> > These drivers allow C code to run on the graphics shares of a
modern
> > video card. These are the same processors that allow high speed 3d
> > graphics. Several math intensive applications report a 50-100 fold
> > performance improvement over running on the host cpu.
> >
> > The mid range card that came on my system has 64 cores, each able
to
> > perform one floating point operation per clock.
> >
> > As a simple test, I wrote a AmiBroker plug in, called by AFL.
> >
> > It calculated the average price (H+L+C)/3 for 60464 bars in 21us.
> >
> > This works out to about 8.5GF (billion floating point operations
per
> > second) and 46GB/s memory transfer speed. (read 3 floats and write
> > one per bar), (2 floating point adds and 1 multiply per bar)
> >
> > The 46GB/s transfer rate is not far from the available memory
> > bandwidth on the card, but the simple test calculation is not
> > very "dense" so, I should be able to get a much higher calculation
> > rate once I move more of my code to the graphics cores. Several of
> > the CUDA demos report > 150GF/s. Memory is the bottleneck of this
> > simple test. I used one thread per bar.
> >
> > High end graphics cards are available now that would improve
> > performance by another factor of 2 to 4.
> >
> > A few problems:
> > * The above numbers do not include the time needed to copy the
data
> > from ami to the graphics card or copy the results back. This time
is
> > much greater than the calculation time in this simple test.
> > * This is not a general AFL accelerator.
> >
> > My goal is to reduce my current 25s backtest time down to < 1s per
> > pass. To do this, I will need to move the data set for all symbols
> > to the graphics card once and make many passes over the data with
> > different optimization values. Each CUDA thread will work on one
> > symbol, rather than a thread per bar as in my first test.
> >
> > There is not much point in writing a CUDA routine to just execute
> > directly from AFL code. There is too much overhead. In my
> > application, the AFL code is a very small part of the total time
for
> > each backtest. Even if I reduced the time to zero, it would not
> > reduce the time per pass very much. Also, the time needed to copy
> > the price data on each pass would greatly reduce the benefit. As
far
> > as I can tell, the current Ami API does not allow injecting a
> > externally generated trade list into the backtest, so I will need
to
> > perform the full backtest and fitness function calculation
> > externally.
> >
> > I had no compatibility problems getting the CUDA api to run as a
Ami
> > plug in.
> >
> > Why go to the trouble? Using Fred's IO program would get much of
the
> > same benefit for less trouble, or I could wait until Ami finally
> > supports multi cores, or finds other clever ways to reduce the per
> > pass overhead. The real answer is that I just had to try it....
> >
> >
>
------------------------------------
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