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

Paul Krueger plkrueger at comcast.net
Thu May 21 14:55:05 UTC 2009


You can't emphasize these points enough. GPGPU technology has its  
place, but it's not perfect for everything. If you have an application  
where data can be partitioned up neatly and distributed to separate  
processing elements which tend to do the same limited things over and  
over (FFT's are a good example), then GPGPU's may be appropriate (as  
are FPGA's for similar reasons, although there are certainly other  
factors there). If you have an application where each processing  
thread may dynamically determine that it needs data from an arbitrary  
location within a very large block of memory or needs to do frequent  
updates within large data blocks in arbitrary ways, then GPGPU's are  
not appropriate because the communication and synchronization costs  
will typically kill you. That's especially true on any larger  
distributed memory architecture, but even on smaller systems you might  
overwhelm the memory subsystem.  Many of the sorts of AI, graph, and  
intelligent applications that I am personally more interested in fall  
into the second category, so GPGPU's will likely not be of much help.

Paul

On May 20, 2009, at 1:06 PM, Dan Weinreb wrote:

> 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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