Compiling 64bit R using Intel Compiler (icc/ifort) and Intel Math Kernel Library (MKL).
通过Intel的编译器和Intel MKL，我们得到运行速度最快的R系统（比上一篇介绍的 R+GotoBlas 还快一点点）。
下载，安装Intel Parallel Studio，这个包括Intel C compiler (icc), C++ Compiler (icpc), Fortran compiler(ifort)：
http://software.intel.com/en-us/articles/intel-parallel-studio-xe/
下载，安装Intel Math Kernel Library
http://software.intel.com/en-us/articles/intel-mkl/
Intel的这两个软件对于非商业用途是免费的。
然后需要下载R的源代码：
http://cran.cnr.berkeley.edu/
解压缩R之后，在其目录下建立bash文件来指定编译的方式（R本身是使用静态链接还是动态链接库？安装路径？）。
具体方式可以在这个脚本的末尾部分找到，大家可以自己按需要修改。
注：在我的比较下，使用动态链接的BLAS库与静态链接库相比不会损失速度；使用动态链接库的优点是可以方便的换用不同BLAS库。
source /home/zhanxw/intel/composer_xe_2011_sp1.6.233/bin/iccvars.sh intel64
source /home/zhanxw/intel/composer_xe_2011_sp1.6.233/bin/ifortvars.sh intel64
source /home/zhanxw/intel/composer_xe_2011_sp1.6.233/mkl/bin/mklvars.sh intel64
export CC=icc
export CFLAGS="-O3 -wd188 -mieee-fp"
export F77=ifort
export FFLAGS="-O3 -mieee-fp"
export CXX=icpc
export CXXFLAGS="-O3"
export FC=ifort
export FCFLAGS="-O3 -mieee-fp"
export ICC_LIBS=/home/zhanxw/intel/composer_xe_2011_sp1.6.233/compiler/lib/intel64
export IFC_LIBS=/home/zhanxw/intel/composer_xe_2011_sp1.6.233/compiler/lib/intel64
export SHLIB_CXXLD=icpc
export SHLIB_CXXLDFLAGS=-shared
MKL_LIB_PATH=/home/zhanxw/intel/composer_xe_2011_sp1.6.233/mkl/lib/intel64
export LD_LIBRARY_PATH=$MKL_LIB_PATH
OMP_NUM_THREADS=8
export LDFLAGS="-L${MKL_LIB_PATH},-Bdirect,--hash-style=both,-Wl,-O1 -L$ICC_LIBS -L$IFC_LIBS -L/usr/local/lib"
export SHLIB_LDFLAGS="-lpthread"
export MAIN_LDFLAGS="-lpthread"
MKL="-L${MKL_LIB_PATH} -lmkl_blas95 -lmkl_lapack95 -Wl,--start-group -lmkl_intel -lmkl_intel_thread -lmkl_core -Wl,--end-group -openmp -lpthread"
OMP_NUM_THREADS=8
MKL="-L${MKL_LIB_PATH} -lmkl_intel_lp64 -lmkl_intel_thread -lmkl_core -liomp5 -lpthread"
#static linked library of R
#./configure --with-blas="$MKL" --with-lapack="$MKL" --prefix=/net/dumbo/home/zhanxw/software/Rmkl
# dynamic linked library of: R and BLAS
#./configure --enable-R-shlib --enable-BLAS-shlib --with-blas="$MKL" --with-lapack="$MKL" --prefix=/net/dumbo/home/zhanxw/software/Rmkl
#dynamic linked library of: BLAS
./configure --enable-BLAS-shlib --with-blas="$MKL" --with-lapack="$MKL" --prefix=/net/dumbo/home/zhanxw/software/Rmkl
之后用make; make install即可。
使用同样的R-benchmark脚本，结果如下：
Intel Compiler (ICC+Ifort) and Intel MKL
R Benchmark 2.5
===============
Number of times each test is run__________________________: 3
I. Matrix calculation
---------------------
Creation, transp., deformation of a 2500x2500 matrix (sec): 0.719666666666667
2400x2400 normal distributed random matrix ^1000____ (sec): 0.394333333333333
Sorting of 7,000,000 random values__________________ (sec): 0.861
2800x2800 cross-product matrix (b = a' * a)_________ (sec): 0.709
Linear regr. over a 3000x3000 matrix (c = a \ b')___ (sec): 0.448
--------------------------------------------
Trimmed geom. mean (2 extremes eliminated): 0.611437229773395
II. Matrix functions
--------------------
FFT over 2,400,000 random values____________________ (sec): 0.907666666666668
Eigenvalues of a 640x640 random matrix______________ (sec): 0.613000000000001
Determinant of a 2500x2500 random matrix____________ (sec): 0.493333333333333
Cholesky decomposition of a 3000x3000 matrix________ (sec): 0.334333333333332
Inverse of a 1600x1600 random matrix________________ (sec): 0.611666666666667
--------------------------------------------
Trimmed geom. mean (2 extremes eliminated): 0.569777440099831
III. Programmation
------------------
3,500,000 Fibonacci numbers calculation (vector calc)(sec): 0.82
Creation of a 3000x3000 Hilbert matrix (matrix calc) (sec): 0.535999999999999
Grand common divisors of 400,000 pairs (recursion)__ (sec): 2.64933333333334
Creation of a 500x500 Toeplitz matrix (loops)_______ (sec): 0.683666666666667
Escoufier's method on a 45x45 matrix (mixed)________ (sec): 0.828000000000003
--------------------------------------------
Trimmed geom. mean (2 extremes eliminated): 0.774276714018349
Total time for all 15 tests_________________________ (sec): 11.609
Overall mean (sum of I, II and III trimmed means/3)_ (sec): 0.646126830621363
--- End of test ---
Intel Compiler(ICC+Ifort) + GotoBlas2(Compiled by ICC/Ifort)
R Benchmark 2.5
===============
Number of times each test is run__________________________: 3
I. Matrix calculation
---------------------
Creation, transp., deformation of a 2500x2500 matrix (sec): 0.715333333333333
2400x2400 normal distributed random matrix ^1000____ (sec): 0.41
Sorting of 7,000,000 random values__________________ (sec): 0.862666666666666
2800x2800 cross-product matrix (b = a' * a)_________ (sec): 0.829333333333333
Linear regr. over a 3000x3000 matrix (c = a \ b')___ (sec): 0.554666666666667
--------------------------------------------
Trimmed geom. mean (2 extremes eliminated): 0.690382674196494
II. Matrix functions
--------------------
FFT over 2,400,000 random values____________________ (sec): 0.922333333333332
Eigenvalues of a 640x640 random matrix______________ (sec): 0.681333333333333
Determinant of a 2500x2500 random matrix____________ (sec): 0.511666666666667
Cholesky decomposition of a 3000x3000 matrix________ (sec): 0.433333333333332
Inverse of a 1600x1600 random matrix________________ (sec): 0.594333333333331
--------------------------------------------
Trimmed geom. mean (2 extremes eliminated): 0.591732764155743
III. Programmation
------------------
3,500,000 Fibonacci numbers calculation (vector calc)(sec): 0.835999999999999
Creation of a 3000x3000 Hilbert matrix (matrix calc) (sec): 0.545000000000002
Grand common divisors of 400,000 pairs (recursion)__ (sec): 2.66133333333333
Creation of a 500x500 Toeplitz matrix (loops)_______ (sec): 0.695666666666665
Escoufier's method on a 45x45 matrix (mixed)________ (sec): 0.585000000000001
--------------------------------------------
Trimmed geom. mean (2 extremes eliminated): 0.698105585240407
Total time for all 15 tests_________________________ (sec): 11.838
Overall mean (sum of I, II and III trimmed means/3)_ (sec): 0.658231817116501
--- End of test ---
常见的错误：
在编译R的时候，我们用–with-blas=”$MKL”来制定Intel MKL的位置 （网上其他文章的做法），但如果$MKL的值不正确，R无法正常链接MKL。我们需要检查configure的输出或者文件config.log，要确保这两项的检查都是yes:
checking for dgemm_ in -L/home/zhanxw/intel/composer_xe_2011_sp1.6.233/mkl/lib/intel64 -lmkl_intel_lp64 -lmkl_intel_thread -lmkl_core -liomp5 -lpthread… yes
checking whether double complex BLAS can be used… yes
checking whether the BLAS is complete… yes
值得指出的是在链接Intel库时，LP64 和 ILP64是不同的。在我的机器上，错误的制定ILP64，例如-lmkl_intel_ilp64，会导致R无法使用MKL，因为使用ILP64编译的程序会crash(在configure脚本里，这个文件是conftest)
config.log是非常有用的文件，它包括的configure检查系统环境时相关信息，通过这个文件并结合configure(本质是一个shell script），可以帮助我们确定R是否可以，或者为什么不可以链接MKL库。
另外，使用shared BLAS库的时候R会检查zgeev_，并检查不到MKL，这个R“有意”的结果。因为动态的MKL库会包含LAPACK的信息。如果介意这方面的速度损失，可以使用静态链接的方式。
Updated (2011-10-05):
Similar idea in the PPT format: