- Use auto in places where code clarity is improved or identical.
- Replace trivial constructors and destructors with default.
- Use true random for random generation.
- Use std::string_view where it is valid to do so.
- Apply const where it is valid to do so.
- Use references where it is valid to do so.
- Manually optimize memory usage with std::move and std::copy.
- Opt for memory efficient containers where the size is known ahead of time.
Signed-off-by: lainon <GermanAizek@yandex.ru>
The CUDA library is always available as a singleton, so it does not make sense for it to be passed in. Instead we can simply grab it from the singleton and use it as it is, which makes the code easier to maintain and automates certain code.
Load additional functions from CUDA and add new enumerations to support them:
* cuDevicePrimaryCtxSetFlags allows us to sched scheduling mode for the GPU.
* cuCtxgetStreamPriorityRange allows us to check which priority levels are supported.
* cuStreamCreateWithPriority allows us to create streams with non-default priority.
The scheduler mode is now set to yield so that other threads can do work when we hit an eventual stalling problem. Streams can also now be created with higher priority and different flags, if necessary. In most cases this should allow CUDA resources to execute even while the GPU is under heavy load.
Due to the 'nvcuda' library being part of the driver, it falls in a clause of the GPL which allows us to load and interface with system drivers. Since we can't rely on Nvidias headers here (incompatible license), most of this was pulled from FFmpeg and other things were found out via testing.
