This sample demonstrates CUDA-NvSciBuf/NvSciSync Interop. Two CPU threads import the NvSciBuf and NvSciSync into CUDA to perform two image processing algorithms on a ppm image - image rotation in 1st thread & rgba to grayscale conversion of rotated image in 2nd thread. Currently only supported on Ubuntu 18.04
CUDA NvSci Interop, Data Parallel Algorithms, Image Processing
SM 6.0 SM 6.1 SM 7.0 SM 7.2 SM 7.5 SM 8.0 SM 8.6
Linux
x86_64, aarch64
cudaImportExternalMemory, cudaExternalMemoryGetMappedBuffer, cudaExternalMemoryGetMappedMipmappedArray, cudaImportExternalSemaphore, cudaSignalExternalSemaphoresAsync, cudaWaitExternalSemaphoresAsync, cudaDestroyExternalSemaphore, cudaDestroyExternalMemory
Download and install the CUDA Toolkit 11.2 for your corresponding platform. Make sure the dependencies mentioned in Dependencies section above are installed.
The Linux samples are built using makefiles. To use the makefiles, change the current directory to the sample directory you wish to build, and run make:
$ cd <sample_dir>
$ make
The samples makefiles can take advantage of certain options:
-
TARGET_ARCH= - cross-compile targeting a specific architecture. Allowed architectures are x86_64, aarch64. By default, TARGET_ARCH is set to HOST_ARCH. On a x86_64 machine, not setting TARGET_ARCH is the equivalent of setting TARGET_ARCH=x86_64.
$ make TARGET_ARCH=x86_64
$ make TARGET_ARCH=aarch64
See here for more details. -
dbg=1 - build with debug symbols
$ make dbg=1
-
SMS="A B ..." - override the SM architectures for which the sample will be built, where
"A B ..."
is a space-delimited list of SM architectures. For example, to generate SASS for SM 50 and SM 60, useSMS="50 60"
.$ make SMS="50 60"
-
HOST_COMPILER=<host_compiler> - override the default g++ host compiler. See the Linux Installation Guide for a list of supported host compilers.
$ make HOST_COMPILER=g++