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43 lines
1.5 KiB
Markdown
43 lines
1.5 KiB
Markdown
# cuSOLVERMp
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NVIDIA [cuSOLVERMp] is a high-performance, distributed-memory, GPU-accelerated library that provides
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tools for the solution of dense linear systems and eigenvalue problems.
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## Dependencies
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[cuSOLVERmp] < 0.7
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- [CAL]: requires `libcal.\*` in the `$PATH`
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- [UCC]: requires `libucc.\*` and `libucs.\*` in the `$PATH`
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[cuSOLVERmp] >= 0.7
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- [NCCL]: requires `libnccl.\*` in the `$PATH`
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## CMake
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[cuSOLVERMp] is enabled with the following CMake option:
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```bash
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-DCP2K_USE_CUSOLVER_MP=ON
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```
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The `FindCuSolverMP.cmake` module tries to automatically deduce the [cuSOLVERmp] version from the
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`cusolvermp.h` header file, and enables the `CP2K_CUSOLVERMP_USE_NCCL` CMake option in case it finds
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cuSOLVERmp >= 0.7.
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## Generalized diagonalization
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The `DIRECT_GENERALIZED_DIAGONALIZATION` global input keyword enables direct generalized
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diagonalization paths that avoid a CP2K-side Cholesky reduction where the selected eigensolver
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supports them. With [cuSOLVERMp], this currently covers real symmetric and complex Hermitian
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generalized eigenproblems through `cusolverMpSygvd`.
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When [cuSOLVERMp] uses the NCCL backend, CP2K relies on the launcher or scheduler to bind MPI ranks
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to GPUs. For example, assigning one GPU per rank with `CUDA_VISIBLE_DEVICES` is supported. CP2K only
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checks that the CUDA device selected for each MPI rank is visible to that rank.
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[cal]: https://developer.download.nvidia.com/compute/cublasmp/redist/libcal/
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[cusolvermp]: https://docs.nvidia.com/cuda/cusolvermp/
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[nccl]: https://developer.nvidia.com/nccl
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[ucc]: https://github.com/openucx/ucc
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