[GTALUG] Running Dell branded Nvidia gtx 1060 in non-dell system

[Alex Volkov]

Hey Nick,

See my replies below

On 2019-08-06 8:09 p.m., xerofoify via talk wrote:
> On Tue, Aug 6, 2019 at 4:23 PM Alex Volkov via talk <talk at gtalug.org> wrote:
>> Yes.
>>
>> Unfortunately I went though the debugging process before I got to the paragraph. On the upside I think I got a lightning talk out of it, that I'll try to present at the next meeting.
>>
>> Alex.
>>
> Alex,
> I don't know how much your intending to do with that GPU or otherwise.
> If your just using Nvidia I can't help
> you as mentioned but if your interested in GPU workloads  was looking
> at the AMDGPU backend for LLVM.
> Not sure if there is one that targets Nvidia cards but it may be of
> interest to you as you would be able to
> compile directly for the GPU rather than using an API to access it.
> Not sure about Nvidia so double check
> that.
>
> Here is the official documentation for AMD through:
> https://llvm.org/docs/AMDGPUUsage.html

I haven't gotten that far into it, just ran a few ML tutorials, I 
haven't yet created any of my own models yet, my knowledge is limited to 
trying out tensorflow package and getting tensorflow-gpu (nvidia 
bindings) verifiably working with the hardware.

Turns out NVIDIA drivers have this nice feature of falling back to CPU 
processing when there's an error -- this is useful when needing to get 
things done at any cost, not so much when attempting to debug the issue.

So far I mostly used the card for hw-accelerated h264 encoding through 
ffmpeg.

> If your using it for machine learning it may be helpful to be aware of
> it as you could compile
> the libraries if possible onto the GPU target rather than access than
> indirectly through
> the CPU. Again not sure of what libraries but you should for most of
> the popular ones
> and that may increase throughput a lot as it's direct assembly for the
> card not abstracted.

Thanks for the advice, I'm not that far along in the process to use this 
information.

You seem to know a lot on the topic of optimizing workloads on GPU, 
would you like to come to our meeting next Tuesday and give a 5-10 
minute talk on this? -- https://gtalug.org/meeting/2019-08/


> As for GPU memory that may be a issue as Hugh mentioned depending on the size
> of the workload. I don't think it would matter for your tutorials but
> going across the
> PCI bus is about as bad as cache misses for CPUs so best to not have them if
> possible. If you were able to find a 6GB version that would be more than enough
> for most workloads excluding professional. 1060s were shipped with either 3 or
> 6GB so that may be something for card you ordered to check. Retail I recall
> it being about a 30-50 Canadian difference and for double the RAM it was
> a good detail at the time if you bought one.

There seem to be a lot of gamers who upgraded to 1080 selling used 1060 
6GB for a reasonable price. I got MSI GTX 1060 6GB version.

So far with h264 encoding I've noticed that there's significant 
processing drop, when the card finishes encoding a chunk of data, then 
saturates PCI bus.


Alex.

>
> Hopefully that helps a little,
>
> Nick
>
> P.S. Not aware but I'm assuming there is one for gcc as well if you would
> prefer that for your development or learning.
>
>> On 2019-08-05 11:12 a.m., D. Hugh Redelmeier via talk wrote:
>>
>> | From: Alex Volkov via talk <talk at gtalug.org>
>>
>> | I have another system with Ryzen 5 2400G and was hoping to run ROCm on it, but
>> | as it turns out -- ROCm doesn't fully support AMD cards with built-in
>> | graphics. I still can install discreet card into that system but the solution
>> | is not as cheap as getting a used GTX off craigslist.
>>
>> In January I saw cheap Radeo RX 580s on Kijiji too.  I haven't looked
>> recently.
>>
>> One advantage of AMD over nvidia is that larger memories are more common.
>>
>> It's a shame about ROCm's lack of APU support.  Parts of it are there.
>>
>> <https://rocm.github.io/hardware.html>
>>
>>      The iGPU in AMD APUs
>>
>>      The following APUs are not fully supported by the ROCm stack.
>>
>> “Carrizo” and “Bristol Ridge” APUs
>> “Raven Ridge” APUs
>>
>>      These APUs are enabled in the upstream Linux kernel drivers and the
>>      ROCm Thunk. Support for these APUs is enabled in the ROCm OpenCL
>>      runtime. However, support for them is not enabled in our HCC compiler,
>>      HIP, or the ROCm libraries. In addition, because ROCm is currently
>>      focused on discrete GPUs, AMD does not make any claims of continued
>>      support in the ROCm stack for these integrated GPUs.
>>
>>      In addition, these APUs may may not work due to OEM and ODM choices
>>      when it comes to key configurations parameters such as inclusion of
>>      the required CRAT tables and IOMMU configuration parameters in the
>>      system BIOS. As such, APU-based laptops, all-in-one systems, and
>>      desktop motherboards may not be properly detected by the ROCm drivers.
>>      You should check with your system vendor to see if these options are
>>      available before attempting to use an APU-based system with ROCm.
>>
>>
>> ---
>> Post to this mailing list talk at gtalug.org
>> Unsubscribe from this mailing list https://gtalug.org/mailman/listinfo/talk
>>
>>
>> ---
>> Post to this mailing list talk at gtalug.org
>> Unsubscribe from this mailing list https://gtalug.org/mailman/listinfo/talk
> ---
> Post to this mailing list talk at gtalug.org
> Unsubscribe from this mailing list https://gtalug.org/mailman/listinfo/talk