Does high-level cuQuantum API work with MPI? #112
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Hi, I browsed the documentation and examples in the repo, but I failed to find any mention or examples of whether one could use high-level tensor network API, such as Related, I wonder if there is any connection between Apologies if I have missed the obvious or did not dive into API ref. deep enough to understand myself! Many thanks! |
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Replies: 2 comments 10 replies
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Yes, the high-level tensor network API fully supports distributed parallel execution via MPI on multiple/many GPUs. The way to activate distributed parallel execution is exactly the same as before (example: ). Of course, this requires a distributed GPU platform with CUDA-aware MPI installed (https://docs.nvidia.com/cuda/cuquantum/latest/cutensornet/api/functions.html#distributed-parallelization-api) as well as other bookkeeping related to MPI initialization, etc. (like here ). Other than that, exactly the same Python (or C++) code can run on both single GPU and multiple/many GPUs without any additional effort from the user. To benefit from the distributed parallel execution, the problem size needs to be sufficiently large. |
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Btw, in your current high-level API documentation (https://docs.nvidia.com/cuda/cuquantum/latest/python/api/cutensornet.html#high-level-tensor-network-api) you seem to have forgotten the |
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Yes, the high-level tensor network API fully supports distributed parallel execution via MPI on multiple/many GPUs. The way to activate distributed parallel execution is exactly the same as before (example:
cuQuantum/python/samples/cutensornet/tensornet_example_mpi_auto.py
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cuQuantum/pytho…