[jmalicki]

I have actually had a ton of success using Strassen matrix multiplication kernels with extra structure in custom CUDA kernels (e.g. a covariance matrix is symmetric positive definite, or can be represented with Cholesky, and that comes up in a ton of useful computation). It's been a couple of years, but IIRC I would find it would start to win over the standard kernels at ~n>2500 or something (and in addition to Strassen was also exploiting the explicit structural constraints of the matrix, so not a completely fair comparison).

[TimorousBestie]

If you’re interested, I found https://arxiv.org/abs/2505.09814v1 to beat Strassen for medium-sized and larger covariance matrices. YMMV of course. Takes a little adjustment for XX^H but it’s not so bad.

[jmalicki]

Thanks!