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37 lines
1.8 KiB
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37 lines
1.8 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) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](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, 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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cudaHostAlloc, cudaMemPrefetchAsync, cudaFree, cudaLaunchCooperativeKernel, cudaMallocManaged, cudaSetDevice, cudaGetDeviceCount, cudaGetDeviceProperties, cudaFreeHost, cudaMemset, cudaStreamCreate, cudaStreamSynchronize, cudaDeviceEnablePeerAccess, cudaMemAdvise, cudaOccupancyMaxActiveBlocksPerMultiprocessor, cudaDeviceCanAccessPeer
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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 12.5](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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## References (for more details)
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