Add util functions for runtime CUDA kernel source compilation
using NVRTC library. Like other nvcodec dependent libraries,
NVRTC library will be loaded via g_module_open.
Note that the NVRTC library naming is not g_module_open friendly
on Windows.
(i.e., nvrtc64_{CUDA major version}{CUDA minor version}.dll).
So users can specify the dll name using GST_NVCODEC_NVRTC_LIBNAME
environment.
Part-of: <https://gitlab.freedesktop.org/gstreamer/gst-plugins-bad/-/merge_requests/1633>
Introducing CUDA buffer pool with generic CUDA memory support.
Likewise GL memory, any elements which are able to access CUDA device
memory directly can map this CUDA memory without upload/download
overhead via the "GST_MAP_CUDA" map flag.
Also usual GstMemory map/unmap is also possible with internal staging memory.
For staging, CUDA Host allocated memory is used (see CuMemAllocHost API).
The memory is allowing system access but has lower overhead
during GPU upload/download than normal system memory.
Part-of: <https://gitlab.freedesktop.org/gstreamer/gst-plugins-bad/-/merge_requests/1633>
We weren't using the correct calling convention when calling CUDA and
CUVID APIs. `CUDAAPI` is `__stdcall` on Windows. This was working fine
on x64 because `__stdcall` is ignored and there's no special calling
convention. However, on x86, we need to use `__stdcall`.
Introduce GstCudaGraphicsResource structure to represent registered
CUDA graphics resources and to enable sharing the information among
nvdec and nvenc. This structure can reduce the number of resource
registration which cause high overhead.
Async CUDA operation with default stream (NULL CUstream) is not much
beneficial than blocking operation since all CUDA operations which belong
to the CUDA context will be synchronized with the default stream's operation.
Note that CUDA stream will share all resources of the corresponding CUDA context
but which can help parallel operation similar to the relation between thread and process
By adding system memory support for nvdec, both en/decoder
in the nvcodec plugin are able to be usable regardless of
OpenGL dependency. Besides, the direct use of system memory
might have less overhead than OpenGL memory depending on use cases.
(e.g., transcoding using S/W encoder)
... and add our stub cuda header.
Newly introduced stub cuda.h file is defining minimal types in order to
build nvcodec plugin without system installed CUDA toolkit dependency.
This will make cross-compile possible.
