[gmx-users] strange GPU load distribution

[Alex]

Thanks Mark. No need to be sorry, a CPU-only build is a simple enough 
fix. Inelegant, but if it works, it's all good. I'll report as soon as 
we have tried.

I myself run things in a way that you would find very familiar, but we 
have a colleague developing forcefields and that involves tons of very 
short CPU-only runs getting submitted in bursts. Hopefully, one day 
you'll be able to accommodate this scenario. :)

Alex


On 5/7/2018 1:13 AM, Mark Abraham wrote:
> Hi,
>
> I don't see any problems there, but I note that there are run-time settings
> for the driver/runtime to block until no other process is using the GPU,
> which may be a contributing factor here.
>
> As Justin noted, if your EM jobs would use a build of GROMACS that is not
> configured to have access to the GPUs, then there can be no problem. I
> recommend you do that if you want to continue sharing this node between GPU
> and non-GPU jobs. There has long been the principle that users must take
> active steps to keep GROMACS processes away from each other when sharing
> CPU resources, and this is a similar situation.
>
> In the abstract, it would be reasonable to organize mdrun so that we
> determine that we might want to use a GPU if we have one before we run the
> GPU detection, however that high-level code is in considerable flux in
> development branches, and we are highly unlikely to prioritise such a fix
> in a stable release branch to suit this use case. I didn't think that some
> of the reorganization since 2016 release would have this effect, but
> apparently it can. Sorry!
>
> Mark
>
> On Mon, May 7, 2018 at 6:33 AM Alex <nedomacho at gmail.com> wrote:
>
>> Mark,
>>
>> I am forwarding the response I received from the colleague who prepared
>> the box for my GMX install -- this is from the latest installation of
>> 2018.1. See text below and please let me know what you think. We have no
>> problem rebuilding things, but would like to understand what is wrong
>> before we pause all the work.
>>
>> Thank you,
>>
>> Alex
>>
>> "OS Ubuntu 16.04LTS
>>
>> After checking gcc and kernel-headers were installed I ran the following
>>
>> #sudo lspci |grep -i nvidia
>>
>> #curl
>>
>> http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.1.85-1_amd64.deb
>>   > cuda-repo-ubuntu1604_9.1.85-1_amd64.deb
>>
>> #sudo dpkg -i cuda-repo-ubuntu1604_9.1.85-1_amd64.deb
>>
>> #sudo apt-key adv
>> --fetch-keyshttp://
>> developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
>>
>> #sudo dpkg -i cuda-repo-ubuntu1604_9.1.85-1_amd64.deb
>>
>> #sudo apt update
>>
>> #sudo apt upgrade
>>
>> #sudo /sbin/shutdown -r now
>>
>> After reboot
>>
>> #sudo apt-get install cuda
>>
>> #export PATH=/usr/local/cuda-9.1/bin${PATH:+:${PATH}}
>>
>> #nvidia-smi
>>
>> I also compiled the samples in the cuda tree using the Makefile there
>> and had no problems."
>>
>> --
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