Fastor VS Google Test

I'm going to throw in a recommendation for Fastor. It is generally faster than other libraries, is very lightweight, and has a pretty modern syntax.

There are C++ libraries that deal with linear algebra and tensors that are able to produce fully vectorized code without requiring you to mess around with SIMD intrinsics. See, for instance, fastor, blaze, eigen and the huge Trillinos set of packages. C++ is very widely used when it comes to scientific HPC applications. All you need to do is google search or better yet, join r/cpp and r/cpp_questions and start asking away for the things you need. The C++ community is very welcoming and full of experts that will be able to help you.

Picking what size you are targeting is really important, though. Could the matrices you are working with realistically be bigger than say 32x32? BLAS is good for big matrices. It's not as great for small matrices. Eigen or Fastor will do better for these smaller problems. And for various common operations on sizes 2, 3, and 4, hand coded graphics-oriented libraries might outperform those.

Tensorflow, Machine learning: https://www.tensorflow.org/ Fastor, A tensor library: https://github.com/romeric/Fastor GNU Scientific Library(GSL): https://www.gnu.org/software/gsl/ Boost. FEniCS, A finite element library: https://fenicsproject.org/ Intel MKL, a BLAS+LAPACK+other goodies library: https://software.intel.com/content/www/us/en/develop/tools/math-kernel-library.html SuiteSparse, A sparse linear algebra library: http://faculty.cse.tamu.edu/davis/suitesparse.html Sundials, Nonlinear solvers: https://computing.llnl.gov/projects/sundials

cmake_minimum_required(VERSION 3.5) project(knn_cpp CXX) include(FetchContent) FetchContent_Declare( googletest GIT_REPOSITORY https://github.com/google/googletest.git GIT_TAG release-1.11.0 ) FetchContent_MakeAvailable(googletest) FetchContent_Declare(matplotplusplus GIT_REPOSITORY https://github.com/alandefreitas/matplotplusplus GIT_TAG origin/master) FetchContent_GetProperties(matplotplusplus) if(NOT matplotplusplus_POPULATED) FetchContent_Populate(matplotplusplus) add_subdirectory(${matplotplusplus_SOURCE_DIR} ${matplotplusplus_BINARY_DIR} EXCLUDE_FROM_ALL) endif() function(knn_cpp_test TEST_NAME TEST_SOURCE) add_executable(${TEST_NAME} ${TEST_SOURCE}) target_link_libraries(${TEST_NAME} PUBLIC matplot) aux_source_directory(${CMAKE_CURRENT_SOURCE_DIR}/../lib LIB_SOURCES) target_link_libraries(${TEST_NAME} PRIVATE gtest gtest_main gmock gmock_main) target_include_directories(${TEST_NAME} PRIVATE ${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_SOURCE_DIR}/../) target_sources(${TEST_NAME} PRIVATE ${LIB_SOURCES} ) include(GoogleTest) gtest_discover_tests(${TEST_NAME}) endfunction() knn_cpp_test(LinearAlgebraTest la_test.cc) knn_cpp_test(KnnTest knn_test.cc) knn_cpp_test(UtilsTest utils_test.cc)

Okay, time to start unit tests!!! We will use Unity Test Framework to do unit testing. It is one of widely used testing frameworks alongside with Check, Google Test etc. Just downloading source code, and putting it to the project folder is enough to make it work (that is also why it is portable).

Updating GCC (it happened to GoogleTest).

Roy's project uses Google Test, a C++ testing framework. His testing setup is similar to mine as we both keep source files in one directory and tests in another. The key difference is that I can run the tests using the Visual Studios run button. It was fairly easy to write the new tests as there were existing ones that I could reference to check the syntax!

The Google Test Documentation provides a primer for first-time users. The primer introduces some basic concepts and terminology, some of which I've been able to learn for this lab exercise.

> git submodule add https://github.com/google/googletest.git > git submodule update --init --recursive

cmake_minimum_required(VERSION 3.14) project(my_project) # GoogleTest requires at least C++14 set(CMAKE_CXX_STANDARD 14) set(CMAKE_CXX_STANDARD_REQUIRED ON) include(FetchContent) FetchContent_Declare( googletest URL https://github.com/google/googletest/archive/03597a01ee50ed33e9dfd640b249b4be3799d395.zip ) # For Windows: Prevent overriding the parent project's compiler/linker settings set(gtest_force_shared_crt ON CACHE BOOL "" FORCE) FetchContent_MakeAvailable(googletest) enable_testing() add_executable( hello_test hello_test.cpp ) target_link_libraries( hello_test GTest::gtest_main ) include(GoogleTest) gtest_discover_tests(hello_test)

FetchContent\_Declare(googletestGIT\_REPOSITORY [[email protected]](mailto:[email protected]):googletest.git [https://github.com/google/googletest.git](https://github.com/google/googletest.git)GIT\_TAG release-1.12.1)FetchContent\_MakeAvailable(googletest)

Not sure about CppUnit but I can speak to my previous experience using the googletest framework which compiles your tests to an executable, and since it's a very simple framework we were able to cross-compile and run directly on our device. We just had to hook up a device to the server that was running the CI so it could flash it when needed. That basically meant that our process was: