Sep 112011
 

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:

R_BLAS-Sachdeva

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