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# if setup does not find the MKL or does not use the configuration, copy this
# file to "site.cfg" and edit the paths according to your installation of the
# Intel MKL.
# Note: some versions of MKL need to be linked to gfortran if compiled with
# GNU C compiler. Uncomment next line if you get an error like "undefined
# symbol: _gfortran_malloc"
#
# Note2: Some Fedora users reported that they had to install a
# compatible version of the gfortran lib. See:
# http://code.google.com/p/numexpr/issues/detail?id=15
# for more info.
[DEFAULT]
#libraries = gfortran
[mkl]
# Example for the MKL included in Intel C 11.0 compiler
# (you may need a recent NumPy version for being able to search libraries
# in different directories at a time)
#library_dirs = /opt/intel/Compiler/11.0/074/mkl/lib/em64t/:/opt/intel/Compiler/11.0/074/lib/intel64
#include_dirs = /opt/intel/Compiler/11.0/074/mkl/include/
#mkl_libs = mkl_solver_ilp64, mkl_intel_ilp64, mkl_intel_thread, mkl_core, iomp5
# This seems to work for MKL 11 with processors with AVX (Sandy Bridge and above) for Linux
#library_dirs = /opt/intel/composerxe/mkl/lib/intel64:/opt/intel/composer_xe_2013.3.163/compiler/lib/intel64
#include_dirs = /opt/intel/composerxe/mkl/include/
#mkl_libs = mkl_intel_lp64, mkl_gf_lp64, mkl_intel_thread, mkl_core, mkl_blas95_lp64, mkl_lapack95_lp64, mkl_avx, mkl_vml_avx, mkl_rt, iomp5
## Example for using MKL 10.0
#library_dirs = /opt/intel/mkl/10.0.2.018/lib/em64t
#include_dirs = /opt/intel/mkl/10.0.2.018/include
# Example for using MKL 10.2 for Windows 64-bit
#include_dirs = \Program Files\Intel\MKL\10.2.5.035\include
#library_dirs = \Program Files\Intel\MKL\10.2.5.035\em64t\lib
#mkl_libs = mkl_solver_ilp64, mkl_core, mkl_intel_thread, mkl_intel_ilp64, libiomp5md
# The next works too, but for LP64 arithmetic
#mkl_libs = mkl_core, mkl_intel_thread, mkl_intel_lp64, libiomp5md
# Example with Intel compiler version 14.0.2 and MKL v11.1.2 on intel64 architecture
#mkl_libs = mkl_intel_lp64, mkl_gf_lp64, mkl_intel_thread, mkl_core, mkl_def, mkl_vml_avx, mkl_rt, iomp5
# For details, see https://github.com/pydata/numexpr/issues/148
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