CMakeLists.txt

cmake_minimum_required (VERSION 3.0)
project(MPI_Project.exe)

# Prevent compilation in-source
if( ${CMAKE_BINARY_DIR} STREQUAL ${PROJECT_SOURCE_DIR} )
  Message( "" )
  Message( FATAL_ERROR "Source and build directories are the same!")
endif()

# Flags for release configuration: 
# -O3:           level 3 compiler optimizations => faster code
# -march-native: compile for the native (=your computer) architecture => faster code
# -ffast-math:   allow "unsafe" match operations, i.e. those operations, where the result of a floating point operation could be unstable
#                This might be a problem for cross platform but deterministic applications (e.g. multiplayer games)
#                allow fast math => faster code
# -fopenmp:      activate openmp support (next lecture), might be enabled by default depending on your compiler
set(CMAKE_CXX_FLAGS_RELEASE "-O3 -march=native -ffast-math -fopenmp")

# Additional 'find module' scipts in the subfolder 'cmake'
# Currently, FindLAPACKE is the important one. FindMKL exist for the case that you need it in another project.
#set(CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/lib/cmake") #/;${CMAKE_MODULE_PATH}")
#message("${CMAKE_CURRENT_LIST_DIR}/lib/cmake/") #;${CMAKE_MODULE_PATH}")
list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/lib/cmake")

# We want parallel code
find_package(OpenMP REQUIRED)

# Open MPI Package
find_package(MPI REQUIRED)

# Basic Linear Algebra Subprogram (BLAS): description of fastest elementary matrix / vector operations
# The Fortran realization of BLAS is provided by 'OpenBlas', and should be the fastest implementation
# without vendor lock.
#
# Intel's Math Kerner Library (MKL) is normally the best choice for even faster linear algebra code,
# but as the name suggests it is not available on AMD Machines. Also our HPC CPUs are too old ...
#
# Capital letters required here and in the following, I learned it the hard way ...
find_package(BLAS REQUIRED)    

# The Linear Algebra Package (LAPACK) makes use of the operations defined in BLAS to provide the actual 
# linear algebra stuff (e.g. equation solver or SVD), Numpy also uses it in the background.
#
# OpenLapack provides the implementation similar to OpenBlas, Intel MKL would be faster and has everything
# included (BLAS + LAPACK + OpenMP), but Intel is greedy and restricts it to their CPUs ...
find_package(LAPACK REQUIRED)  

# Interface for Eigen between C++ and Fortran: 
# Eigen, a C++ library, requires some code to communicate with the Fortran implementation of BLAS and LAPACK
# Pay attention to the E in the end.
find_package(LAPACKE REQUIRED)

# CNPY needs zlib
find_package(ZLIB REQUIRED)

# Path to cnpy directory
set(CNPY_DIR lib/cnpy)

add_subdirectory(${CNPY_DIR})

# Add Include directories
include_directories(${MPI_INCLUDE_PATH} 
                    ${ZLIB_INCLUDE_DIRS} 
                    ${LAPACKE_INCLUDE_DIRS_DEP} 
                    ${CMAKE_CURRENT_LIST_DIR}/lib/Eigen/
                    ${CMAKE_CURRENT_LIST_DIR}/lib/
                    ${CMAKE_CURRENT_LIST_DIR}/src/
                    ${CNPY_DIR} 
                    )

# Create executable as usual
file(GLOB SRC_FILES src/*.cpp main.cpp)
add_executable(${PROJECT_NAME} ${SRC_FILES} src/vi_processor_impl_distr_05.cpp src/vi_processor_impl_distr_05.h)

#add_dependencies(${PROJECT_NAME} boost)
target_link_libraries(${PROJECT_NAME} 
                      ${MPI_LIBRARIES} 
                      ${ZLIB_LIBRARIES} 
                      OpenMP::OpenMP_CXX 
                      ${LAPACKE_LIBRARIES_DEP} 
                      cnpy
                      )