# cuDLAStandaloneMode - cuDLA Standalone Mode

## Description

This sample demonstrates cuDLA standalone mode wherein DLA can be programmed without using CUDA.

## Key Concepts

cuDLA, 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 8.9 ](https://developer.nvidia.com/cuda-gpus)  [SM 9.0 ](https://developer.nvidia.com/cuda-gpus)

## Supported OSes

Linux, QNX

## Supported CPU Architecture

aarch64

## CUDA APIs involved

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

## Prerequisites

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

## References (for more details)
