Re: [Discuss-gnuradio] GNURadio and CUDA reprised

[Tom Rondeau]

On Wed, Jan 12, 2011 at 3:39 PM, Marcus D. Leech <address@hidden> wrote:
>> On Jan 12, 2011, at 2:56 PM, Moeller wrote:
>> The "very large FIR filters" was a thought, as an example of an operation
>> that might benefit from a GPU at least when using OpenCL (or CUDA).  I
>> haven't done testing yet to know if a GPU can do better than a CPU using
>> vector instructions ... but I'm getting there.  If/when I do get there, I'll
>> post my results&  thoughts.
>>
> Very large FFT filters is also something worth looking into.  GPUs have been
> considered for real-time coherent de-dispersion of radio astronomy
>  data streams for pulsar detection.  De-dispersion over large bandwidths at
> low frequencies requires ferociously-large FFT filters, but in
>  order to make this a viable proposition, you likely have to do the
> detection and folding on the GPU as well, producing an output data
>  stream that is several orders of magnitude smaller/slower than the input
> stream.  I read a paper on this, (for the specific case of
>  pulsar detection with real-time coherent de-dispersion), and they concluded
> that it's doable, on the higher end GPUs, provided that
>  you do detection and folding on the GPU as well, otherwise you lose due to
> transfer overhead.
>
> It seems like the only time you ever really "win" with a GPU-based solution
> is when you have to suck in large amounts of data,
>  pound on it furiously, and then produce an output stream that's relatively
> modest.  Otherwise, you seem to lose due to data-transfer
>  overhead.
> --
> Marcus Leech
> Principal Investigator
> Shirleys Bay Radio Astronomy Consortium
> http://www.sbrac.org


>From my experiments, I don't thinks its a A _and_ B situation. I think
if you have either A) a large amount of data _OR_ B) have to pound on
it furiously, you get a win. Most filters needed for normal comms is
not enough data or computation, but doing, maybe, a turbo product code
or some heavy compute task with normal amounts of data (say, blocks of
around 8k samples), you can get a win.

Tom