Eigendecomposition using cuSolver

Thread starter Pablo Brubeck
Start date Sep 21, 2015
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eigenvectors

In summary, Eigendecomposition using cuSolver is a method for finding eigenvalues and eigenvectors of a square matrix using the cuSolver library in CUDA. It works by converting the matrix into Hessenberg form, allowing for parallelization on GPUs, and using an iterative algorithm to find the eigenvalues and eigenvectors. Some benefits of this method include faster computation time, efficient handling of large and sparse matrices, and the option for parallelization. It is commonly used in machine learning, computer vision, physics, and engineering applications. However, it may not be suitable for small matrices and may require some knowledge of CUDA programming.

Sep 21, 2015

Pablo Brubeck

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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Sep 24, 2015

Greg Bernhardt

Admin

Insights Author

Have you looked through the documentation? http://docs.nvidia.com/cuda/cusolver

Sep 27, 2015

Pablo Brubeck

Just found out through this keynote, the eigendecomposition for dense matrices is under development and will be featured on a future release.

1. What is Eigendecomposition using cuSolver?

Eigendecomposition using cuSolver is a method for finding the eigenvalues and eigenvectors of a square matrix using the cuSolver library in CUDA. It is mainly used for solving large, sparse matrices efficiently on GPUs.

2. How does Eigendecomposition using cuSolver work?

Eigendecomposition using cuSolver works by first converting the given matrix into a special form called the Hessenberg form. This reduces the computation time and allows for parallelization on GPUs. Then, an iterative algorithm is used to find the eigenvalues and eigenvectors of the Hessenberg matrix.

3. What are the benefits of using Eigendecomposition using cuSolver?

Eigendecomposition using cuSolver offers several benefits, including faster computation time compared to traditional CPU methods, the ability to handle large and sparse matrices efficiently, and the option for parallelization on GPUs. This makes it a useful tool for scientific and engineering applications.

4. What are some common applications of Eigendecomposition using cuSolver?

Eigendecomposition using cuSolver is commonly used in various fields, such as machine learning, computer vision, physics, and engineering. It is particularly useful for solving eigenvalue problems in quantum mechanics, image processing, and data analysis.

5. Are there any limitations to Eigendecomposition using cuSolver?

While Eigendecomposition using cuSolver has many benefits, it also has some limitations. It is most efficient for large and sparse matrices, and may not be suitable for small matrices. Additionally, it may require some knowledge of CUDA programming for optimal use.