R with MKL on Windows 10

Following up on my previous post, a commenter on Reddit asked to see a comparison with Microsoft R Open using multithreaded Intel MKL:

Microsoft R Open 3.2.5
Default CRAN mirror snapshot taken on 2016-05-01
The enhanced R distribution from Microsoft
Visit https://mran.microsoft.com/ for information
about additional features.

Multithreaded BLAS/LAPACK libraries detected. Using 4 cores for math algorithms.

RcppEigen, RcppArmadillo & RcppGSL were all installed from MRAN as binary packages. As expected, MKL outperforms GotoBLAS:

lm benchmark for n = 1650 and p = 875: nrep = 20
   user system elapsed 
1908.28  27.55 1870.58 

   test relative elapsed user.self sys.self
3  LDLt    1.000    4.61      4.44     0.18
7  QR      1.386    6.39      6.12     0.25
8  LLt     1.460    6.73      6.42     0.32
1  lm.fit  1.631    7.52      7.40     0.08
9  arma    2.206   10.17     37.87     2.70
4  GESDD   2.560   11.80     43.82     2.86
6  SymmEig 5.514   25.42     25.14     0.26
2  PivQR   9.857   45.44     25.26    20.15
5  SVD   188.666  869.75    868.87     0.42
10 GSL   191.262  881.72    881.45     0.27

There are none of the multi-threading issues that I observed in the previous benchmark. Elapsed time for lm.fit improves from 18.11 to 7.52, arma improves from 90.03 to 10.17, and GESDD improves from 90.77 to 11.80 seconds (in comparison to default libRblas). Compared with single-threaded SurviveGotoBLAS, MKL gives a 3x speedup for RcppArmadillo and GESDD. The other methods all perform roughly the same, since they do not rely on the BLAS library.