conjugateGradientMultiDeviceCG - conjugateGradient using MultiDevice Cooperative Groups

Description

This sample implements a conjugate gradient solver on multiple GPUs using Multi Device Cooperative Groups, also uses Unified Memory optimized using prefetching and usage hints.

Key Concepts

Unified Memory, Linear Algebra, Cooperative Groups, MultiDevice Cooperative Groups, CUBLAS Library, CUSPARSE Library

Supported SM Architectures

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 8.9 SM 9.0

Supported OSes

Linux, Windows

Supported CPU Architecture

x86_64, aarch64

CUDA APIs involved

CUDA Runtime API

cudaHostAlloc, cudaMemPrefetchAsync, cudaFree, cudaLaunchCooperativeKernel, cudaMallocManaged, cudaSetDevice, cudaGetDeviceCount, cudaGetDeviceProperties, cudaFreeHost, cudaMemset, cudaStreamCreate, cudaStreamSynchronize, cudaDeviceEnablePeerAccess, cudaMemAdvise, cudaOccupancyMaxActiveBlocksPerMultiprocessor, cudaDeviceCanAccessPeer

Dependencies needed to build/run

UVM, MDCG, C++11 CUDA

Prerequisites

Download and install the CUDA Toolkit for your corresponding platform. Make sure the dependencies mentioned in Dependencies section above are installed.

References (for more details)