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Can a test for the python API of Lanczos also be added?
| handle, | ||
| v0_vector_const, | ||
| V_0_view, | ||
| [device_scalar = v0nrm_scalar.data_handle()] __device__(auto y) { return y / *device_scalar; }); |
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Can v0nrm and it's copy operations be skipped this way?
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| [device_scalar = v0nrm_scalar.data_handle()] __device__(auto y) { return y / *device_scalar; }); | |
| [device_scalar = output1.data_handle()] __device__(auto y) { return y / *device_scalar; }); |
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I unfortunately don't have permissions to push on the branch of this PR so I kept the commits and continued on PR #2481. |
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I unfortunately don't have permissions to push on @aamijar branch for the previous Lanczos solver PR (#2416) so I kept his commits and continued it here. ## Lanczos Solver for Sparse Eigen Decomposition We propose a new lanczos solver in raft that fixes the issues present in the previous solver `raft::sparse::solver::detail::computeSmallestEigenvectors`. Specifically we address the following issues: 1. Numerical Stability for both float32 and float64 datatypes 2. Efficiency and Speed of Convergence This new implementation is taken from the cupy library `cupyx.scipy.sparse.linalg.eigsh` where the thick-restart and full reorthogonalzation methods are used. Additionally this PR exposes a python api for raft lanczos solver with an interface similar to `scipy.sparse.linalg.eigsh` and `cupyx.scipy.sparse.linalg.eigsh`. ```py3 from pylibraft.solver import eigsh ``` Authors: - Micka (https://github.com/lowener) - Anupam (https://github.com/aamijar) - Corey J. Nolet (https://github.com/cjnolet) Approvers: - Kyle Edwards (https://github.com/KyleFromNVIDIA) - Corey J. Nolet (https://github.com/cjnolet) URL: #2481
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Lanczos Solver for Sparse Eigen Decomposition
We propose a new lanczos solver in raft that fixes the issues present in the previous solver
raft::sparse::solver::detail::computeSmallestEigenvectors.Specifically we address the following issues:
This new implementation is taken from the cupy library
cupyx.scipy.sparse.linalg.eigshwhere the thick-restart and full reorthogonalzation methods are used.Additionally this PR exposes a python api for raft lanczos solver with an interface similar to
scipy.sparse.linalg.eigshandcupyx.scipy.sparse.linalg.eigsh.