[Openmcl-devel] Speed, compilers and multi-core processors
plkrueger at comcast.net
Thu May 21 07:55:05 PDT 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.
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:
>>> 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
>>> 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
>> 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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