Hi Guys,
Admittedly I can run benchmarks for this sort of thing, but I think an authoritative answer from the authors is better.
1. What if any impact does the row/column major storage have on the various matrix decomposition algorithms in Eigen? I imagine, that QR factorization for example will do better if the storage is column major.
2. Are the matrix decomposition algorithms affected by the alignment of the underlying storage, or more generally, do the matrix decompositions use SIMD vectorization which in turns require alignment of the underlying data?
In this case I did do some benchmarking, where I used an eigen Matrix and a block of memory allocated using new and wrapped inside a Map object, and performed cholesky factorization on numerically identical (row major matrices) and did not notice any difference. Thus the curiosity. It is entirely possible that my benchmark is broken.
Thanks,
Sameer