### Sequential SVD computation for Big Data using MAGMA

Posted:

**Fri Dec 01, 2017 11:23 am**I've been away from this forum for a while! I have a new 1080 ti and am contemplating putting it through its paces by computing a large SVD calculation. So large, it is likely to need a sequential strategy, if possible. I noted the recent paper "Out of Memory SVD Solver for Big Data" (Sept. 2017) on the icl.cs.utk.edu website.

My question is what is the present situation with a practical implementation on a device such as what I have? The platform is an i7 6850K (6 core) with 32 GB RAM. The matrices I have in mind to compute the eigenvectors/values of are 10MX10M, say (speaking ambitiously) and they are a covariance, hence they are real, square, single precision and have mirror upper and lower values. I really would just need a small subset of eigenvectors (1000?) - those with the larges eigenvalues (I might perhaps start with smaller, more modest, matrices in getting started...)

Any comments/suggestions on how to get started in computing such a problem? Perhaps not yet possible?

Thx,

B-C

My question is what is the present situation with a practical implementation on a device such as what I have? The platform is an i7 6850K (6 core) with 32 GB RAM. The matrices I have in mind to compute the eigenvectors/values of are 10MX10M, say (speaking ambitiously) and they are a covariance, hence they are real, square, single precision and have mirror upper and lower values. I really would just need a small subset of eigenvectors (1000?) - those with the larges eigenvalues (I might perhaps start with smaller, more modest, matrices in getting started...)

Any comments/suggestions on how to get started in computing such a problem? Perhaps not yet possible?

Thx,

B-C