
CPU, GPU Put to Deep Learning Framework Test - adamnemecek
http://www.nextplatform.com/2016/09/01/cpu-gpu-put-deep-learning-framework-test/
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amelius
Am I the only one who hates the GPU form factor? Basically, I guess most
people just want a co-processor box that they can attach to, and place on top
of their PC. And link another one to it, et cetera. No clumsiness with power
cables inside the confined space of the PC. No running out of space after
adding 3 units. (GPUs occupy two PCI slots because of their bulky size).

So I have two questions:

\- Do we really need the speed of PCI to connect to GPUs, or would a lower
speed connection (USB 3/firewire) be sufficient for most computational
applications such as deep learning?

\- How would the performance of these deep learning frameworks scale as we add
more and more units?

~~~
quantumhobbit
The speed Of the interconnect is absolutely critical. It depends on the
problem somewhat, but if you are moving data back and forth cpu to gpu the
interconnect becomes the limiting factor very quickly. So much of a limitation
that for some problems you might be better of with a cpu or a puny gpu that is
on the same die as the cpu.

If you can do everything on the gpu then it isn't a problem but at that point
why isn't the gpu your main processor?

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gumby
> If you can do everything on the gpu then it isn't a problem but at that
> point why isn't the gpu your main processor?

The GPU is a highly optimized block of SIMD machines with a more limited set
of ALU ops and addressing modes. The CPU has a more general architecture.

It's totally reasonable to have a truck, a car and a bicycle.

~~~
quantumhobbit
Modern CPUs have multiple cores so they are really four cars bolted together.
Why not bolt a car and a truck together and let them share memory (he says
completely ruining the metaphor).

Graphics cards are becoming full fledged computers, a sibling poster mentioned
AMD adding an interface for SSds to their cards. So why have the big x86 multi
core system if all it does is feed data to the symbiotic computer that is the
gpu and give us a shell prompt?

I guess I just answered my question. Gpus are symbiots because they don't run
Linux. But I would like to see one that does, whatever that would look like.

~~~
detaro
That would probably look (roughly) like Intel Xeon Phi, which according to
some was started as a GPU project. (they are also co-processors, but you can
connect to one and log into a Linux running on it)

I'm not sure why you'd totally want to get rid of a general-purpose CPU to run
Linux, set up data etc and keep the GPU part simpler (and fast) once you've
given them equal memory access.

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haldora
Do note the OS and CUDA version differences between the 980, 1080 and K80
tests. Like the last deep learning comparison posted on HN, they failed to get
a consistent baseline system. I don't know how much this would affect
performance, but it should be considered.

\- 980: Ubuntu 14.04; CUDA 7.5

\- 1080: Ubuntu 14.04; CUDA 8.0

\- K80: CentOS 7.2; CUDA 7.5

~~~
shepardrtc
The benchmark is not very good because of this. I hope no one takes this
seriously. They don't even use the same BLAS libraries for all of the
frameworks.

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sxp
It's strange that they're using a 2012 i7-3820 instead of a modern i7. They're
also missing price when comparing a ~$300 i7, 2x $700 Xeons, a $650 GTX 1080,
and a $4k K80.

~~~
shepardrtc
That's not the only inconsistency in it. Different OS'es, BLAS libraries,
driver versions.

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rayuela
The person responsible for making the table with the benchmarks needs to be
taken out back and shot. Goddamnit, who makes a table with 286 figures and
only uses lines of equal width throughout the whole goddamn thing. Also, I
second haldora's qualms about the lack of consistent software used to make
these hardware comparisons.

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jbmorgado
It's quite interesting too see as a consumer card (the GTX 1080) outperforms a
professional and much more expensive one (the Tesla K80) by a good margin.

~~~
dharma1
The K80 uses 2 generations older architecture (Kepler)

