Eigendecomposition using cuSolver

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SUMMARY

The discussion focuses on obtaining eigenvalues and eigenvectors for dense, non-hermitian matrices using cuSolver, a CUDA library from NVIDIA. Users are advised to consult the official cuSolver documentation for guidance. It is noted that eigendecomposition for dense matrices is currently under development and will be included in a future release of the library. This indicates that users may need to wait for updates to fully utilize this feature.

PREREQUISITES
  • Familiarity with CUDA programming
  • Understanding of eigenvalues and eigenvectors
  • Knowledge of dense matrix operations
  • Access to cuSolver documentation
NEXT STEPS
  • Review the latest cuSolver documentation for updates on eigendecomposition
  • Explore alternative libraries for eigenvalue computations, such as cuBLAS
  • Study CUDA programming best practices for optimizing matrix operations
  • Monitor NVIDIA's announcements for future releases related to cuSolver features
USEFUL FOR

Researchers, data scientists, and developers working with CUDA who need to perform eigenvalue computations on dense, non-hermitian matrices.

Pablo Brubeck
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I am looking for a clear example on how to obtain the complete set of eigenvalues and eigenvectors for a dense, non-hermitian matrix using cuSolver.
 
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Just found out through this keynote, the eigendecomposition for dense matrices is under development and will be featured on a future release.
 

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