[FFmpeg-user] When using h264_cuvid, memory allocation differs between different nvidia GPUs

[Panagiotis Malakoudis]

I did more tests and there is a consistent difference in VRAM usage when
running same commands in GTX 1050 Ti and GTX 1070 Ti.
NVENC encoding also excibits same behaviour, for example:
ffmpeg -f mpegts -i https://samples.ffmpeg.org/V-codecs/h264/HD-h264.ts
-vcodec h264_nvenc -c:a copy -f mpegts transcoded.ts
all defaults, allocates 94MB in 1050 Ti but 163MB in 1070 Ti, for a
difference of 69MB.
Combining encoding AND decoding in one command, for example:
ffmpeg -hwaccel cuvid -c:v h264_cuvid -f mpegts -i
https://samples.ffmpeg.org/V-codecs/h264/HD-h264.ts -vcodec h264_nvenc -c:a
copy -f mpegts transcoded.ts
1070 Ti: 269MB, 1050 Ti: 200MB.
So we see a difference of 66MB when only decoding, 69MB when only encoding,
69MB when both decoding+encoding.
I also tested two different driver versions, 384.130 and 390.77, both give
same results.
What is interesting is that testing in Windows gives different, lower VRAM
values for GTX 1070 Ti but still more than GTX 1050 Ti in Linux (I couldn't
test GTX 1050 Ti in Windows yet). For example, decoding uses 132MB when
same command in Linux uses 153MB.

I don't think it is a bug in ffmpeg, probably something going on in nvidia
drivers. Where I could report this to nvidia?


Στις Πέμ, 13 Σεπ 2018 στις 3:49 μ.μ., ο/η Panagiotis Malakoudis <
malakudi at gmail.com> έγραψε:

> Lowering surfaces in GTX 1070 Ti to 2 only reduces memory to 129MB, so it
> is still not the same as GTX 1050 Ti. Also, 2 surfaces are not enough for
> correct decoding, decoding has errors.
> Using nvdec with command:
> ffmpeg -hwaccel nvdec -f mpegts -i
> https://samples.ffmpeg.org/V-codecs/h264/HD-h264.ts -vcodec libx264
> -preset veryfast -crf 23 -c:a copy -f mpegts -y output.ts
> uses even more memory, 321MB on GTX 1070 Ti compared to 153MB with
> h264_cuvid.
> -surfaces is not applicable on -hwaccel nvdec
>
>
>
>
> Στις Πέμ, 13 Σεπ 2018 στις 1:28 μ.μ., ο/η Dennis Mungai <dmngaie at gmail.com>
> έγραψε:
>
>> Hello there,
>>
>> On the system using more VRAM, can you lower the -surfaces value from 8 to
>> ~6?
>>
>> Also, in place of h264_cuvid, use nvdec instead.
>>
>> Example:
>>
>> ffmpeg -c:v nvdec -surfaces 8 -f mpegts -i
>> https://samples.ffmpeg.org/V-codecs/h264/HD-h264.ts -vcodec libx264
>> -preset
>> veryfast -crf 23 -c:a copy -f mpegts transcoded.ts
>>
>> On Thu, 13 Sep 2018 at 11:31, Panagiotis Malakoudis <malakudi at gmail.com>
>> wrote:
>>
>> > I am using h264_cuvid in some systems. GTX10xx cards have the power to
>> > decode many full hd streams concurrently.
>> > I have found that memory allocated in the GPU differs between GTX 1050
>> Ti
>> > and GTX 1070 Ti. With the same command, same settings, ffmpeg allocates
>> > only 87MB in GTX 1050 Ti while it allocates 153MB in GTX 1070 Ti. There
>> is
>> > an exact 66MB difference which is critical in my application if it can
>> be
>> > avoided. Is there any reason why this happens and can it be avoided?
>> >
>> > Example command:
>> > ffmpeg -c:v h264_cuvid -surfaces 8 -f mpegts -i
>> > https://samples.ffmpeg.org/V-codecs/h264/HD-h264.ts -vcodec libx264
>> > -preset
>> > veryfast -crf 23 -c:a copy -f mpegts transcoded.ts
>> > In GTX 1050 Ti, nvidia-smi output:
>> >
>> >
>> +-----------------------------------------------------------------------------+
>> > | Processes:                                                       GPU
>> > Memory |
>> > |  GPU       PID   Type   Process name                             Usage
>> >   |
>> >
>> >
>> |=============================================================================|
>> > |    0     28243      C   ffmpeg
>> > 87MiB |
>> >
>> >
>> +-----------------------------------------------------------------------------+
>> > In GTX 1070 Ti, nvidia-smi output:
>> >
>> >
>> +-----------------------------------------------------------------------------+
>> > | Processes:                                                       GPU
>> > Memory |
>> > |  GPU       PID   Type   Process name                             Usage
>> >   |
>> >
>> >
>> |=============================================================================|
>> > |    0     10291      C   ffmpeg
>> >  153MiB |
>> >
>> >
>> +-----------------------------------------------------------------------------+
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