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68 lines
3.5 KiB
Markdown
68 lines
3.5 KiB
Markdown
# cudaNvSci - CUDA NvSciBuf/NvSciSync Interop
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## Description
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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
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## Key Concepts
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CUDA NvSci Interop, Data Parallel Algorithms, Image Processing
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## Supported SM Architectures
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[SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus)
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## Supported OSes
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Linux
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## Supported CPU Architecture
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x86_64, aarch64
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## CUDA APIs involved
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### [CUDA Driver API](http://docs.nvidia.com/cuda/cuda-driver-api/index.html)
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cuDeviceGetUuid
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### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html)
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cudaGetMipmappedArrayLevel, cudaImportNvSciImage, cudaImportExternalSemaphore, cudaNvSciApp, cudaStreamCreateWithFlags, cudaExternalMemoryGetMappedMipmappedArray, cudaNvSciWait, cudaDestroyExternalMemory, cudaMemcpyAsync, cudaStreamDestroy, cudaSignalExternalSemaphoresAsync, cudaDeviceGetNvSciSyncAttributes, cudaFreeMipmappedArray, cudaMallocHost, cudaNvSci, cudaImportExternalMemory, cudaSetDevice, cudaImportNvSciRawBuf, cudaImportNvSciSemaphore, cudaGetDeviceCount, cudaDestroyTextureObject, cudaDeviceGetAttribute, cudaDestroyExternalSemaphore, cudaStreamSynchronize, cudaNvSciSignal, cudaFree, cudaDeviceId, cudaExternalMemoryGetMappedBuffer, cudaCreateTextureObject, cudaFreeHost, cudaWaitExternalSemaphoresAsync
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## Dependencies needed to build/run
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[NVSCI](../../README.md#nvsci)
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## Prerequisites
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Download and install the [CUDA Toolkit 11.6](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
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Make sure the dependencies mentioned in [Dependencies]() section above are installed.
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## Build and Run
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### Linux
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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:
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```
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$ cd <sample_dir>
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$ make
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```
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The samples makefiles can take advantage of certain options:
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* **TARGET_ARCH=<arch>** - cross-compile targeting a specific architecture. Allowed architectures are x86_64, aarch64.
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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.<br/>
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`$ make TARGET_ARCH=x86_64` <br/> `$ make TARGET_ARCH=aarch64` <br/>
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See [here](http://docs.nvidia.com/cuda/cuda-samples/index.html#cross-samples) for more details.
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* **dbg=1** - build with debug symbols
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```
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$ make dbg=1
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```
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* **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, use `SMS="50 60"`.
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```
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$ make SMS="50 60"
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```
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* **HOST_COMPILER=<host_compiler>** - override the default g++ host compiler. See the [Linux Installation Guide](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#system-requirements) for a list of supported host compilers.
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```
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$ make HOST_COMPILER=g++
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```
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## References (for more details)
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