# Call Intel MKL as external library

**URL:** <https://discourse.julialang.org/t/call-intel-mkl-as-external-library/2097>\
**Category:** General Usage\
**Created:** [February 14, 2017, 1:30pm UTC](https://discourse.julialang.org/t/call-intel-mkl-as-external-library/2097 "2017-02-14T13:30:00Z")\
**Posts on this page:** 1\
**Showing post:** 3

<div class="post-metadata">

**Author:** ![Tecnezio](https://avatars.discourse-cdn.com/v4/letter/t/ed655f/32.png) [@Tecnezio](https://discourse.julialang.org/u/Tecnezio)\
**Post date:** [February 14, 2017, 2:04pm UTC](https://discourse.julialang.org/t/call-intel-mkl-as-external-library/2097/3 "2017-02-14T14:04:38Z")

</div>

No, i haven’t set anything…

But i loaded the wrong version of the script… and now it work!  
Here the working version:

```julia
# Test MKL solve linear systems

N = 10000;
A = rand(N,N);
AA = copy(A);
b = rand(N,1);
c = copy(b);

function juliaSol(A,b)
 \(A,b);
end
@time sol = juliaSol(A,b);

# Test MKL
const global librt = Libdl.find_library(["libmkl_rt"], ["/opt/intel/mkl/lib"])

# Open librt
Libdl.dlopen(librt)
# LU FACORIZATION
function luFactMKL(A::StridedMatrix{Float64})
  m, n = size(A)
  lda = max(1,stride(A, 2))
  ipiv = similar(A, Int64, min(m,n))

# lapack_int LAPACKE_dgetrf (int matrix_layout , lapack_int m , lapack_int n ,
# double * a , lapack_int lda , lapack_int * ipiv );
  dd = ccall(("LAPACKE_dgetrf", librt), Cint,
            (Int64, Int64, Int64,
            Ptr{Float64}, Int64, Ptr{Int64}),
            102, m, n,
            A, lda, ipiv
  )
  A, ipiv
end

@time AA,pivot = luFactMKL(AA)

# 101 = LAPACK_ROW_MAJOR
# 102 = LAPACK_COL_MAJOR
function linSOLVE(A::StridedMatrix{Float64}, b::StridedMatrix{Float64}, ipiv::StridedVector{Int64})
  m, n = size(A)
  lda = max(1,stride(A, 2))
  #lapack_int LAPACKE_dgetrs (int matrix_layout , char trans , lapack_int n ,
  # lapack_int nrhs , const double * a , lapack_int lda ,
  # const lapack_int * ipiv , double * b , lapack_int ldb );
  dd = ccall(("LAPACKE_dgetrs", librt),
              Cint, # Return type
                (Int64, Cuchar, Int64,
                Int64, Ptr{Float64}, Int64,
                Ptr{Int64}, Ptr{Float64}, Int64),
                102, 'N', m,
                1, A, lda,
                ipiv, b, m
                )
  dd, b, ipiv # dd = 0 if calculation is done, b = output
end

@time dd, sol2, piv = linSOLVE(AA,c, pivot)

```

Using Intel MKL dgetrf + dgtrs julia is as fast as Matlab ! 😉

On my PC: with A = rand(10000,10000);

```julia
Julia Version 0.5.0
Commit 3c9d753 (2016-09-19 18:14 UTC)
Platform Info:
  System: NT (x86_64-w64-mingw32)
  CPU: Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz
  WORD_SIZE: 64
  BLAS: libopenblas (USE64BITINT DYNAMIC_ARCH NO_AFFINITY Prescott)
  LAPACK: libopenblas64_
  LIBM: libopenlibm
  LLVM: libLLVM-3.7.1 (ORCJIT, broadwell)

Mkl "dgetrf" Factorization time 
 12.180575 seconds (4.16 k allocations: 261.296 KB)

JULIA LAPACK factorization time 
 38.910369 seconds (10 allocations: 78.469 KB)

```

LAPACK is very memory efficient… but MKL is 3 times faster (and use 3 times more memory)

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