Monte Carlo simulation using NVIDIA's CURAND library for random number generation

Description

Monte Carlo simulation project by using NVIDIA's CURAND library for random number generation. Cover CUDA aspects: constant memory, random number generation, kernel timing, minimising device memory bandwidth requirements.

Key Concepts

Constant memory; Random number generation; Kernel timing, Minimising device memory bandwidth requirements.

Supported SM Architectures

SM 3.5 SM 3.7 SM 5.0 SM 5.2 SM 5.3 SM 6.0 SM 6.1 SM 7.0 SM 7.2 SM 7.5 SM 8.0 SM 8.6 SM 8.7 SM 9.0

Supported OSes

Linux, Windows

Supported CPU Architecture

x86_64, ppc64le, armv7l

CUDA APIs involved

CUDA Runtime API

cudaMalloc, cudaMemcpy, cudaFree

Prerequisites

Download and install the CUDA Toolkit 11.8 for your corresponding platform.

Build and Run

Windows

The Windows samples are built using the Visual Studio IDE. Solution files (.sln) are provided for each supported version of Visual Studio, using the format:

*_vs<version>.sln - for Visual Studio <version>

Each individual sample has its own set of solution files in its directory:

To build/examine all the samples at once, the complete solution files should be used. To build/examine a single sample, the individual sample solution files should be used.

Note: Some samples require that the Microsoft DirectX SDK (June 2010 or newer) be installed and that the VC++ directory paths are properly set up (Tools > Options...). Check DirectX Dependencies section for details."

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:

$ make

The samples makefiles can take advantage of certain options:

• TARGET_ARCH= - cross-compile targeting a specific architecture. Allowed architectures are x86_64, ppc64le, armv7l. 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=ppc64le
$ make TARGET_ARCH=armv7l
  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, use SMS="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++

References (for more details)

[1] CUDA C++ Programming Guide, https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html