Solving linear equations during inverse iteration

I am using OpenCL to calculate the eigenvectors of a matrix. AMD has an example of eigenvalue calculation so I decided to use inverse iteration to get the eigenvectors.

I was following the algorithm described here and I noticed that in order to solve step 4 开发者_运维技巧I need to solve a system of linear equations (or calculate the inverse of a matrix).

What is the best way to do this on a GPU using OpenCL? Are there any examples/references that I should look into?

EDIT: I'm sorry, I should have mentioned that my matrix is symmetric tridiagonal. From what I have been reading this could be important and maybe simplifies the whole process a lot

The fact that the matrix is tridiagonal is VERY important - that reduces the complexity of the problem from O(N^3) to O(N). You can probably get some speedup from the fact that it's symmetric too, but that won't be as dramatic.

The method for solving a tridiagonal system is here: http://en.wikipedia.org/wiki/Tridiagonal_matrix_algorithm.

Also note that you don't need to store all N^2 elements of the matrix, since almost all of them will be zeroes. You just need one vector of length N (for the diagonal) and two of length N-1 for the sub- and superdiagonals. And since your matrix is symmetric, the sub- and superdiagonals are the same.

Hope that's helpful...

I suggest using LU decomposition. Here's example.

It's written in CUDA, but I think, it's not so hard to rewrite it in OpenCL.