Performance and LinkingΒΆ

For optimal performance, the target program has to be linked against a BLAS implementation. The BLAS implementation that we install by default with conda is OpenBLAS, but other options, such as MKL are available on conda, too. If xtensor-blas was not installed from conda, the user has to manually verify that a BLAS and LAPACK implementation is available. If you want to fallback to a slower, more generic BLAS implementation, you can use the compile time define -DXTENSOR_USE_FLENS_BLAS.

In order to link against OpenBLAS from CMake, the following lines have to be added to the CMakeLists.txt file.

add_definitions(-DHAVE_CBLAS=1)

if (WIN32)
    find_package(OpenBLAS REQUIRED)
    set(BLAS_LIBRARIES ${CMAKE_INSTALL_PREFIX}${OpenBLAS_LIBRARIES})
else()
    find_package(BLAS REQUIRED)
    find_package(LAPACK REQUIRED)
endif()

message(STATUS "BLAS VENDOR:    " ${BLA_VENDOR})
message(STATUS "BLAS LIBRARIES: " ${BLAS_LIBRARIES})

target_link_libraries(your_target_name ${BLAS_LIBRARIES} ${LAPACK_LIBRARIES})

If CMake is not used, the flags can be passed manually to e.g. g++:

g++ test.cpp -o test -lblas -llapack -DHAVE_CBLAS=1