[Openmcl-devel] Speed, compilers and multi-core processors

[Dan Weinreb]

The instruction set is very restricted, and the communication
paths aren't there, as you suggested.  GPGPU is especially
good for highly compute-intensive operations over not
all that much data.  An FFT is an obvious example but
there are many, many good examples.  (Not that I'm an
expert, but I do know that much.)

There are CUDA-compatible devices that don't even
have a video connection, i.e. for GPGPU only.
The NVidia Tesla, called a "computing processor"
(weird name).  240 cores per board, and you can
chain together four of them.

(My officemates are getting this info and telling to
me faster than I can type it in.  Thanks, Andrew
and Scott.)

-- Dan

Jeremy Jones wrote:
> On Wed, May 20, 2009 at 9:13 AM, Raffael Cavallaro
> <raffaelcavallaro at mac.com> wrote:
>   
>> tomshardware.com ran this a couple of days ago:
>>
>> <http://www.tomshardware.com/reviews/nvidia-cuda-gpgpu,2299.html>
>>
>> It's a summary of real-world results from apps using Nvidia's CUDA.
>> For certain things, like video encoding, they're seeing a 4x speedup
>> using the GPU over using the CPU. In addition, when they use the GPU,
>> it leaves the CPU free for other tasks.
>>     
>
>
> Why don't we just throw out the main CPU and fill our computers with
> graphics cards?  (Once CCL is ported to GPUs of course)
>
> Seriously though, what does a CPU have that a GPU doesn't, besides a
> different instruction set?  More memory?  Better i/o?  Is the GPU
> instruction set too specialized?  I bet the answer is mainly software,
> like OSes and device drivers.  I remember in the old days it was
> common to have a separate processor to handle i/o.  Maybe that's what
> the main CPU should be relegated to.  OTOH, if the software is good
> enough, it should just be distributed to whatever computing resources
> are appropriate and available.  Just thinking out loud.
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