CUDA works best if you load the image and then perform multiple
processing/transformations on it. This is the approach we take in our
architecture.
CUDA uses task queue. It adds the tasks to the queue and assigns a core as
soon as one is available. It uses some form of matrix for preparing the
tasks.
The best approach IMHO is to use the CPU in a multithreaded filtering
application to prepare the tasks, and add them to the queue, while the GPU
just executes the tasks. then each CPU thread waits for its result while
other threads are preparing the next set of data. This is the approach we
take currently. In essence there is no timesharing in GPU Core. Each core
will start working on a task and will not interrupt before completing it.
This is different than a CPU MT. The standard MT is hardly needed when you
have 240 cores in a single board, and you can join boards with external bus
to share the queue ;-) . I think the current limit is 3 boards so you can
scale up to 720 cores in a single system. I have only one board, but it is
probably enough for me to do development.
With best regards,
Boian Mitov
-------------------------------------------------------
Mitov Software
www.mitov.com
-------------------------------------------------------
----- Original Message -----
From: "Marco van de Voort" <[EMAIL PROTECTED]>
To: "FPC developers' list" <[email protected]>
Sent: Tuesday, November 04, 2008 12:48 AM
Subject: Re: [fpc-devel] Parallel Computing
Well, we stuff lighter cards (think 8500,8600) in our vision systems
(passively cooled is better for factory use).
We are also playing with CUDA, but one of the problems we have is that you
must do pretty heavy transformations to earn back just the transfer of
data
to and from videocard memory. Those are too rare for us to dive into that
technology quickly, but I'm keeping notes :)
However if one is more pattern recognition oriented, I can understand that
it could get interesting.
btw, is CUDA MT, as in that you can have several independant calculations
on
the same GPU ?
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