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#1825 closed task (fixed)

multi window nvfbc

Reported by:	Antoine Martin	Owned by:	Antoine Martin
Priority:	major	Milestone:	2.3
Component:	encodings	Version:	2.2.x
Keywords:		Cc:

Description

Split from ticket:1801#comment:5.

It should be possible to use a single nvfbc capture instance for multiple monitors and just adjust the buffer offsets.

Change History (6)

comment:1 Changed 3 years ago by Antoine Martin

Status:	new → assigned

Basic support added in r19137.

Still TODO:

capture only once for all monitors
handle suspend + resume without closing capture
fix segfault when we make the buffer copy narrower (smaller dst_pitch) - waste of GPU RAM, but not a huge problem since we don't copy more than we need
keep track of cuda buffers we allocate and prevent memory overcommit somehow - re-use buffers?
when shutting down, free the buffer so we don't try to use it after the cuda context is destroyed
support multi-window mode in the "sys" backend
nvenc is not used in some cases? (dual 4k too large for nvenc if dimension is miscalculated: individual windows can be encoded at 4k)

Last edited 3 years ago by Antoine Martin (previous) (diff)

comment:2 Changed 3 years ago by Antoine Martin

Resolution:	→ fixed
Status:	assigned → closed

Updates:

r19138: capture only once for all monitors
r19145: use correct image wrapper dimensions: fixes crash when copying also fixes nvenc not being used, keep track of image wrappers to avoid using cuda context after we clean it up
r19146: narrower buffer copy
r19147: "sys" backend support

comment:3 Changed 3 years ago by Antoine Martin

Detected a memory leak during testing for #1823.
Only occurs with nvfbc CUDA buffers and nvenc.
Downloading to system memory before re-uploading to a CUDA buffer with XPRA_NVENC_GPU_MEMCOPY=0 (introduced in r19151) does not help.
Using a non-nvfbc source for video does not leak either.

r19153 fixes this but I'm not 100% sure why that is. The image wrapper method was being called before this change, just maybe not from the right thread or not with the correct CUDA context.