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75 lines
3.8 KiB
Markdown
75 lines
3.8 KiB
Markdown
# conjugateGradientMultiDeviceCG - conjugateGradient using MultiDevice Cooperative Groups
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## Description
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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.
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## Key Concepts
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Unified Memory, Linear Algebra, Cooperative Groups, MultiDevice Cooperative Groups, CUBLAS Library, CUSPARSE Library
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## Supported SM Architectures
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[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)
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## Supported OSes
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Linux, Windows
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## Supported CPU Architecture
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x86_64, ppc64le, aarch64
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## CUDA APIs involved
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### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html)
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cudaDeviceEnablePeerAccess, cudaMemset, cudaFree, cudaMallocManaged, cudaMemPrefetchAsync, cudaHostAlloc, cudaOccupancyMaxActiveBlocksPerMultiprocessor, cudaStreamCreate, cudaGetDeviceCount, cudaFreeHost, cudaSetDevice, cudaDeviceCanAccessPeer, cudaLaunchCooperativeKernel, cudaStreamSynchronize, cudaMemAdvise, cudaGetDeviceProperties
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## Dependencies needed to build/run
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[UVM](../../../README.md#uvm), [MDCG](../../../README.md#mdcg), [CPP11](../../../README.md#cpp11)
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## Prerequisites
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Download and install the [CUDA Toolkit 11.6](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
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Make sure the dependencies mentioned in [Dependencies]() section above are installed.
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## Build and Run
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### Windows
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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:
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```
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*_vs<version>.sln - for Visual Studio <version>
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```
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Each individual sample has its own set of solution files in its directory:
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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.
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> **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."
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### Linux
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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:
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```
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$ cd <sample_dir>
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$ make
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```
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The samples makefiles can take advantage of certain options:
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* **TARGET_ARCH=<arch>** - cross-compile targeting a specific architecture. Allowed architectures are x86_64, ppc64le, aarch64.
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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/>
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`$ make TARGET_ARCH=x86_64` <br/> `$ make TARGET_ARCH=ppc64le` <br/> `$ make TARGET_ARCH=aarch64` <br/>
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See [here](http://docs.nvidia.com/cuda/cuda-samples/index.html#cross-samples) for more details.
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* **dbg=1** - build with debug symbols
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```
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$ make dbg=1
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```
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* **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"`.
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```
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$ make SMS="50 60"
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```
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* **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.
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```
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$ make HOST_COMPILER=g++
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```
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## References (for more details)
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