[Openmcl-devel] Speed, compilers and multi-core processors
dlw at itasoftware.com
Wed May 20 18:06:08 UTC 2009
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
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:
>> 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.
> Openmcl-devel mailing list
> Openmcl-devel at clozure.com
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