The thing that I like with Armadillo is that you can just pick the BLAS implementation that is the fastest on your platform and run with it, while still being high-level compared to BLAS.
[1] http://gcdart.blogspot.co.uk/2013/06/fast-matrix-multiply-an...
Just in case this is useful info, Eigen and Blaze works the same way, Blitz++ doesn't. Blaze seems to be doing the best in the benchmarks.
> OpenBLAS is pretty much on par with Intel MKL
I wont be surprised. I have seen ATLAS beat MKL on some BLAS functions on my runs. Sparse matrix multiply is particularly badly implemented in MKL (atleast was). One could beat their sparse multiply by using multiple instance of their own sparse matrix vector multiplies.
But the reason I mentioned ICC is that there is much more to SIMD than just linear algebraic operations. Non-linear operations that come up quite frequently in stats/ML are sin, cos, exp, log, tanh etc on vectors. GCC/G++ does a decent job now (the best part is that it emits info why it wasn't able to vectorize a particular loop. This lets you restructure the loops to ai the compiler), but ICC still rules.
I was under the impression that Eigen only works with MKL?
Enables the use of external BLAS level 2 and 3 routines (currently works with Intel MKL only)
Source: http://eigen.tuxfamily.org/dox/TopicUsingIntelMKL.html
But the reason I mentioned ICC is that there is much more to SIMD than just linear algebraic operations.
Indeed. I compiled some stuff with ICC, but it did not give me an tangible improvement over the latest GCCs. But for those projects, linear operations were the vast majority.
Re GCC, the same here. ICC is mighty expensive.
... that makes us co-sufferers then, or brothers in masochism :) if you will