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Comment by srean | Hacker News Reader

I saw you mention Armadillo a few times, seems you are having a lot of fun with it. Another somewhat look-alike is Eigen, its pretty nice too. These are all modern takes on the original sin Blitz++ which is actually more full featured in what they can represent than Eigen, Armadillo, mublas (by Boost) and Blaze: it supports multidimensional arrays as opposed to just (2d) matrices. Where Blitz++ falls short of the competition is full use of SIMD instructions. However, G++ does a plenty good job of vectorization, but the king of the hill is still Intel's compiler.

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I am not sure about Intel's compiler, but OpenBLAS is pretty much on par with Intel MKL in most benchmarks I have seen recently, e.g. [1][2].

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...

[2] https://github.com/tmolteno/necpp/issues/18

> you can just pick the BLAS implementation that is the fastest on your platform and run with it

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.

Just in case this is useful info, Eigen and Blaze works the same way, Blitz++ doesn't.

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.

Its a BLAS library standard API that you are linking to, so the vendor doesn't matter so much (in theory). In practice, the amount of annoyance that you have to go through so that it links correctly can vary a lot. Even MKL did not implement all of BLAS / Lapack and that would lead to some linking errors.

Re GCC, the same here. ICC is mighty expensive.

I know that BLAS is a standard API, I switch between BLAS implementations fairly often :). I suspected that Eigen relied on some non-BLAS functions in MKL, since they explicitly state that only MKL works.

> I switch between BLAS implementations fairly often

... that makes us co-sufferers then, or brothers in masochism :) if you will

Not so surprising that OpenBLAS beat MLK on an AMD processor. Although it's definitely keeping up.

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