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Support (nonsymmetric) np.linalg.eig on GPU

See original GitHub issue

Dear jax team,

this is just a friendly bump on the implementation of eigendecomposition and batched SVD on GPU. Are you planning on implementing these?

Should I want to implement it myself, would I be able to do it with the primitives in jax.lax, or would I have to hook up a new part of cuSolver? I am willing to spend the time as I would benefit a lot from these features, but I have no experience with expanding jax and would not know where to look.

Issue Analytics

State:
Created 4 years ago
Reactions:3
Comments:19 (12 by maintainers)

Top GitHub Comments

9reactions

mganahlcommented, Sep 18, 2020

Hi! Just popping up to ask if there is any progress regarding eig. I’m currently preparing a JAX implementation of implicitly restarted arnoldi (non-symmetric operators). The working CPU implementation relies on jax.numpy.linalg.eig to compute eigenvalues of the Hessenberg matrix returned by Arnoldi. Would be great to have this run on GPU eventually.

5reactions

drscookcommented, Sep 29, 2022

+1 for GPU-support for nonsymmetric eig to allow GPU-enabled numpy.roots

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