# cudaNvSci - CUDA NvSciBuf/NvSciSync Interop

## Description

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

## Key Concepts

CUDA NvSci Interop, Data Parallel Algorithms, Image Processing

## Supported SM Architectures

[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)  [SM 9.0 ](https://developer.nvidia.com/cuda-gpus)

## Supported OSes

Linux

## Supported CPU Architecture

x86_64, aarch64

## CUDA APIs involved

### [CUDA Driver API](http://docs.nvidia.com/cuda/cuda-driver-api/index.html)
cuDeviceGetUuid

### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html)
cudaExternalMemoryGetMappedBuffer, cudaImportExternalSemaphore, cudaDeviceGetAttribute, cudaNvSciSignal, cudaGetMipmappedArrayLevel, cudaImportNvSciRawBuf, cudaSetDevice, cudaImportNvSciImage, cudaNvSciApp, cudaDeviceId, cudaMallocHost, cudaSignalExternalSemaphoresAsync, cudaCreateTextureObject, cudaFreeHost, cudaNvSci, cudaNvSciWait, cudaGetDeviceCount, cudaMemcpyAsync, cudaStreamCreateWithFlags, cudaExternalMemoryGetMappedMipmappedArray, cudaStreamDestroy, cudaDeviceGetNvSciSyncAttributes, cudaDestroyTextureObject, cudaDestroyExternalMemory, cudaImportExternalMemory, cudaDestroyExternalSemaphore, cudaFreeMipmappedArray, cudaFree, cudaStreamSynchronize, cudaWaitExternalSemaphoresAsync, cudaImportNvSciSemaphore

## Dependencies needed to build/run
[NVSCI](../../../README.md#nvsci)

## Prerequisites

Download and install the [CUDA Toolkit 12.0](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
Make sure the dependencies mentioned in [Dependencies]() section above are installed.

## Build and Run

### Linux
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=<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.<br/>
`$ make TARGET_ARCH=x86_64` <br/> `$ make TARGET_ARCH=aarch64` <br/>
    See [here](http://docs.nvidia.com/cuda/cuda-samples/index.html#cross-samples) 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, use `SMS="50 60"`.
    ```
    $ make SMS="50 60"
    ```

*  **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.
```
    $ make HOST_COMPILER=g++
```

## References (for more details)