
Installing Nvidia CUDA on Ubuntu 14.04 for Linux GPU Computing - shogunmike
http://www.quantstart.com/articles/Installing-Nvidia-CUDA-on-Ubuntu-14-04-for-Linux-GPU-Computing
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hughdbrown
Would this be significantly easier if there were just a Dockerfile with all
this stuff in it and then the instructions would be, "docker build -t cuda .
&& docker run -t -i cuda /bin/sh"? Or maybe make it a trusted docker image and
developers could just pull/run it?

Later: perhaps not. Graphics drivers and the like rely on an interactive
terminal and an X11 system, AFAICT.

~~~
andrewguenther
I think this could actually be done if the container is run in "\--privileged"
mode.

~~~
hughdbrown
I think it is more than that. The installation has steps that fail because of
terminal requirements at install-time:

    
    
        Unpacking x11-common (from .../x11-common_1%3a7.6+12ubuntu2_all.deb) ...
        debconf: unable to initialize frontend: Dialog
        debconf: (TERM is not set, so the dialog frontend is not usable.)
        debconf: falling back to frontend: Readline
        debconf: unable to initialize frontend: Readline
        debconf: (This frontend requires a controlling tty.)
        debconf: falling back to frontend: Teletype
    

Have a look at the github repo:

[https://github.com/hughdbrown/docker-
cuda](https://github.com/hughdbrown/docker-cuda)

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wernerb
I thought HFT was moving toward FPGA? Does anyone know where/how GPU-HPC is
used?

~~~
m_mueller
First of all, HFT is by far not the only place for HPC. GPU/Intel MIC HPC
usage is still growing in scientific computing. Then, when we look at the
financial world, there's still lots of interest in GPGPU, but rather on the
more heavy weight analysis side. Not everything needs to be done in single
digit milliseconds, there's decisions that can still be done in mere seconds
where you'd rather have a more general purpose capable approach that's
relatively easy to program - which is where GPUs come in, since their
programmability becomes increasingly easy. I know the company that released
the first full blown F# port of CUDA, mainly for the financial sector:
[https://quantalea.net/](https://quantalea.net/). They can even integrate that
stuff with Excel so the bankers can have tons of calculations in their
spreadsheets and still have the graphs update in realtime when they use their
pretty sliders ;-).

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stuaxo
Gah, just make nvidia support for ubuntu that works properly

~~~
shogunmike
Indeed, I had quite a bit of trouble getting it to work prior to this. The
main issue seemed to be compatibility with Ubuntu packaged Nvidia drivers of
various versions. It's certainly not as straightforward as it could be.

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stefantalpalaru
Installing the same version of the same package on Gentoo ~amd64:

    
    
        emerge nvidia-cuda-sdk

~~~
shogunmike
If only it was this easy in Ubuntu :-)

~~~
truncate
Well, its that easy if you can live with slightly old version. There is CUDA
package in official repo IIRC.

~~~
_delirium
Yep, just _apt-get install nvidia-cuda-toolkit_. It's currently v5.5.22 (a
mid-2013 version). Debian started working on a packaging of v6.x in the
"experimental" repository a few weeks ago, which will probably migrate to
regular Debian and Ubuntu releases once it's tested a bit.

~~~
shogunmike
I actually tried this a few months ago. I had some issues trying to run the
typical vector addition "Hello world!" examples on my system.

I'm not 100% sure but I also think that by installing the nvidia-cuda-toolkit
package it leaves out the CUDA samples. These contain the deviceQuery and
bandwidthTest scripts necessary to check that CUDA is functional.

Admittedly I have two consumer cards in SLI, so that may have affected the
install. It could also have been incompatibility between the actual Nvidia
display drivers and the various dependencies. It's a bit messy!

