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Re: [amibroker] Cuda prototype using 64 cores on graphics card



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This is very interesting ... AB Dll's are one thing ... Do you think it's possible to run individual instances of AB itself with CUDA ?

----- Original Message -----
From: dloyer123
Date: Thursday, July 24, 2008 3:13 pm
Subject: [amibroker] Cuda prototype using 64 cores on graphics card
To: amibroker@xxxxxxxxxxxxxxx

> 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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