# MC_EstimatePiInlineQ - Monte Carlo Estimation of Pi (inline QRNG)

## Description

This sample uses Monte Carlo simulation for Estimation of Pi (using inline QRNG).  This sample also uses the NVIDIA CURAND library.

## Key Concepts

Random Number Generator, Computational Finance, CURAND Library

## Supported SM Architectures

[SM 5.0 ](https://developer.nvidia.com/cuda-gpus)  [SM 5.2 ](https://developer.nvidia.com/cuda-gpus)  [SM 5.3 ](https://developer.nvidia.com/cuda-gpus)  [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, Windows

## Supported CPU Architecture

x86_64, ppc64le, armv7l

## CUDA APIs involved

### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html)
cudaMemcpy, cudaGetErrorString, cudaFree, cudaSetDevice, cudaGetDeviceCount, cudaMalloc, cudaFuncGetAttributes, cudaGetDeviceProperties

## Dependencies needed to build/run
[CURAND](../../../README.md#curand)

## Prerequisites

Download and install the [CUDA Toolkit 12.5](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)