STStefanKarpinski·Nov 29, 2013
While it's great having fast kernels, there is a huge amount of computing that can't be crammed into a matrix multiply or any other high-performance kernel. If your assertion were even remotely true, there wouldn't be so many Python, Matlab and R extensions written in C, and things like Cython [1] and Numba [2] wouldn't exist. Sometimes you just want to write a for loop or use recursion and not have it be dog slow. Being "allowed" to use iteration and recursion without a performance penalty is one of the things that Julia provides.
As to not being able to beat MKL, it isn't even necessarily hands down the best BLAS around. For example, OpenBLAS [3] is about as good as MKL, depending on what you're doing. They each have their strengths and weaknesses:
1. OpenBLAS is faster than MKL in all the level-1 tests for small numbers of threads (1-4
Read the rest
). The difference is larger for smaller problems. In small level-2 and level-3 instances, however, MKL does better. Specifically in the case of matrix-vector products, MKL seems to do much better.
2. On various linear algebra tests with LAPACK, MKL is faster for smaller problem sizes, whereas OpenBLAS is faster for larger problems.
3. In general, MKL seems to have better tuning for threads. OpenBLAS, on the other hand, has optimized kernels for LU and Cholesky factorizations, which is what GotoBLAS [4] – on which OpenBLAS is based – did too.
Blake Johnson, who is a regular Julia contributor, did an excellent analysis of this, complete with pretty Gadfly-generated graphs, which can be found in the discussion of this issue: https://github.com/JuliaLang/julia/issues/3965. Interestingly, I believe that Kazushige Goto, who originally created GotoBLAS, now works on MKL at Intel.
[1] http://cython.org
[2] http://numba.pydata.org
[3] https://github.com/xianyi/OpenBLAS
[4] http://en.wikipedia.org/wiki/GotoBLAS