librapid 0.2.13

A fast math and neural network library for Python and C++

Credits

Thanks to @TheWindoof for his help on the library. He and I collaborate to provide the best user experience possible, as well as to produce innovative new ideas and faster, more efficient code.

Why Use Librapid?

In short, LibRapid aims to allow for faster mathematical calculations, including multidimensional arrays, machine learning and arbitrary-precision arithmetic. LibRapid also includes some helper functions to reduce dependencies, such as basic colour manipulation and access to console properties. The entire library is highly optimised and supports GPU calculations where applicable.

How it Works

LibRapid is a highly-optimized C++ (and CUDA) library which can be found at ./src/librapid. The C++ library is interfaced with Python using PyBind11, meaning very little performance is lost between the C++ backend and Python frontend of the library. LibRapid also makes use of some of Agner Fog's libraries and optimisations to accelerate low-level functions and increase performance.

LibRapid also aims to provide a consistent interface between the C++ and Python libraries, allowing you to use the library comprehensively in both languages without having to trawl through two sets of documentation.

Please note that the Python interface has been adjusted slightly from the C++ interface to provide a more "pythonic" feel without reducing the overall functionality.

Installing LibRapid

Python

To install LibRapid as a Python library, simply run pip install librapid in the command line. Hopefully, there will be precompiled wheels available for your operating system and python version, meaning you will not need a C++ compiler to install it (if this is not the case, a modern C++ compiler will be required)

Building from Source

To enable CUDA support, to use your own BLAS library or to get a (potentially) more optimised install of the library, you will have to build LibRapid from source:

git clone https://github.com/LibRapid/librapid.git --recursive
cd librapid
pip install . -vvv

C++

To use the library in C++, you have a few options. The first option is to download the zip file for librapid, copy + paste all of the source and header files and add them all to your project.

You can also use librapid in your CMake projects by either using the FetchContent feature, or by adding the librapid subdirectory:

Using FetchContent

add_executable(MyApp myapp.cpp)

include(FetchContent)
FetchContent_Declare(librapid GIT_REPOSITORY https://github.com/librapid/librapid.git)
FetchContent_MakeAvailable(librapid)

target_link_libraries(MyApp librapid)

Using add_subdirectory

# Clone the library into your project
git clone https://github.com/librapid/librapid.git --recursive

add_executable(MyApp myapp.cpp)

add_subdirectory(librapid)

target_link_libraries(MyApp librapid)

Documentation

Viewing Online

Documentation can be found online here: https://librapid.readthedocs.io/en/latest/

Building from Source

If you would like to build it yourself, you will need to instal the required software, which can be found below:

Some python packages are required to build the docs. These can be installed by running pip install -r docs/requirements.txt

You will also need to install a recent version of Doxygen, which you can find here

To build the documentation, open a command line window in the docs directory and run make html. You can then open the file docs/build/html/index.html in a web-browser to view the documentation.

Performance

LibRapid has highly optimized functions and routines, meaning your code will be faster as a result. Nearly all functions exceed the performance of NumPy and equivlent libraries, and functions are being optimised all the time. Anything slower than NumPy is concidered a bug, and LibRapid developers will attempt to optimise it until they are satisfied with the performance.

Both the C++ and Python libraries are designed to work with any CBLAS compatible library, such as ATLAS or OpenBLAS, though will be fully functional without one, using built-in, but slower, routines.

Parallel Code

LibRapid is designed to use multiple theads to run code faster. The number of threads used for these operations will default to the number of threads available on the system, however this may end up leading to slower code when the number of threads exceeds 12-16. To set the number of threads, use the following functions:

// C++
librapid::setNumThreads(<num>); // Set threads to <num>. <num> must be positive integer
librapid::getNumThreads();      // Get number of threads. If OpenMP was not found, returns 1

# Python
librapid.setNumThreads(<num>); # Set threads to <num>. <num> must be positive integer
librapid.getNumThreads();      # Get number of threads. If OpenMP was not found, returns 1

The CMakeLists.txt file will attempt to find a Blas installation automatically, though it may fail if the files are not strucutred correctly (see below).

If you build LibRapid from source, it will automatically search some specific directories for a BLAS install, though if one is not found, BLAS will not be linked and internal routines will be used instead. Please note that the BLAS install must have the following file structure:

blas-dir
├── include
|   └── cblas.h
├── lib
|   └── your-blas-lib.lib
└── bin (Only on Windows)
    └── your-blass-dll.dll

Recommended Setup for Optimal Performance

The recommended Blas library to use is OpenBLAS, though it is very slow and tedious to build (especially on Windows). For this reason, pre-build binaries are provided which are optimised for most processors and architectures. To download these, go to https://github.com/LibRapid/librapid/actions and select the most recent build (don't worry if it failed!). Scroll down to the bottom of the page and download the zip file for your operating system:

OpenBLAS on macos-latest    :   MacOS
OpenBLAS on ubuntu-latest   :   Linux (not just Ubuntu)
OpenBLAS on windows-latest  :   Windows

Unzip this and put the contents in C:/opt/OpenBLAS on Windows or /opt/OpenBLAS on Linux and MacOS. The folder should look similar to this (example is for Windows):

<root directory> # C:/, /, etc.
└── opt
    ├── bin
    │   └── openblas.dll
    ├── include
    │   └── openblas
    │       ├── cblas.h
    │       ├── f77blas.h
    │       ├── lapack.h
    │       ├── lapacke.h
    │       ├── lapacke_config.h
    │       ├── lapacke_example_aux.h
    │       ├── lapacke_mangling.h
    │       ├── lapacke_utils.h
    │       ├── openblas
    │       │   └── lapacke_mangling.h
    │       └── openblas_config.h
    ├── lib
    │   ├── openblas.lib
    │   └── pkgconfig
    │       └── openblas.pc
    └── share
        └── cmake
            └── OpenBLAS
                ├── OpenBLASConfig.cmake
                ├── OpenBLASConfigVersion.cmake
                ├── OpenBLASTargets-release.cmake
                └── OpenBLASTargets.cmake

With OpenBLAS setup in this way, LibRapid will automatically find and use it, whether your in C++ or building from source for Python. This will (most likely) also give the best performance for the library.