AccRQA

AccRQA is a multi-platform (CPU, NVIDIA GPUs) application that calculates RQA metrics. Acceleration using GPUs is optional and is not required to have an NVIDIA GPU. AccRQA offers high-performance implementation of RQA metrics that are available through Python, R, and C/C++.

The interfaces of the AccRQA library are designed platform and number precision agnostic. Change of the computational platform requires a change of a single parameter in the function invocation. If the selected computational platform is unavailable, the AccRQA library automatically switches to CPU. Therefore, transitioning from a locally run workflow (for example, on a laptop) to a more powerful desktop of an HPC cluster with GPUs is effortless.

Supported platforms are CPUs and NVIDIA GPUs. We are planning to support AMD GPUs in the future.

Interfaces assume that data are resident in the main memory (CPU RAM) and are transferred to the computational platform of the user's choice.

AccRQA library exposes three functions to the user. These functions calculate:

• RR
• LAM, including TT, TTmax
• DET, including L, Lmax, ENTR

AccRQA library supports Euclidean and maximal norms with plans to provide more in the future.

Documentation

The documentation is on GitHub pages. (https://kadamek.github.io/AccRQA/index.html)

How to install Python package

We encourage you to install the accrqa package from the source package, as Python binary wheels poorly support GPUs.

pip install accrqa

Binary wheels can be downloaded from GitHub release page.

The Python package can be uninstalled using:

 pip3 uninstall accrqa

Install from local repository

From the top-level directory, run the following commands to install the Python package:

 pip3 install .

The compiled library will be built as part of this step, so it does not need to be installed separately. If extra CMake arguments need to be specified, set the environment variable CMAKE_ARGS first, for example:

 CMAKE_ARGS="-DCUDA_ARCH=7.0" pip3 install .

Installing on Windows

To install accrqa Python package on Windows from source you need to have

Windows requirements:

• MSVC compiler (part of Visual Studio)
• Make for Windows

To install from PiPy using pip:

 py -m pip install accrqa

To install from the local repository in command line:

 py -m pip install .

How to Install the R Package

System Requirements

To install and use the AccRQA R package, the following system requirements apply:

• R version: 4.0 or newer.
• Operating system: Linux, macOS, or Windows.
• Build tools:
  • On Linux/macOS: gcc, g++, make (usually provided by build-essential).
  • On Windows: Rtools.
• (Optional) CUDA Toolkit (e.g., 11.x or newer) — required for GPU acceleration.
  • Ensure nvcc and cuda_runtime.h are available in the environment.
  • Set CUDA_HOME if the toolkit is not in the default path.

You can install the AccRQA R package either from a downloaded source archive (e.g., .tar.gz or .zip) or directly within RStudio.

Option 1: Install from .zip or .tar.gz in RStudio

1. Open RStudio.
2. Go to Tools → Install Packages.
3. From the box Install from select "Package Archive File (.tar.gz / .zip)" as the sources.
4. Click Browse, and select the downloaded file (e.g., AccRQA_x.y.z.tar.gz where the x.y.z refers to the specific version number).
5. Click Install.

Make sure that Rtools (on Windows) or build-essential (on Linux) is installed to compile packages from source.

Option 2: Install from the Command Line

The downloaded archive can be install by using the R console:

r

# From inside R
install.packages("AccRQA_x.y.z.tar.gz", repos = NULL, type = "source")

or from your system's terminal:

bash

R CMD INSTALL AccRQA_x.y.z.tar.gz

How to Install the C library (Linux)

To configure the build system using CMake, create a build directory

mkdir build

and then

cd build/

run CMake

cmake ../

The CUDA architecture can be specified with the -DCUDA_ARCH flag. For example, for architecture 7.0, do

cmake -DCUDA_ARCH="7.0" ../

To compile binding to R, you have to specify R library location using -DCMAKE_R_LIB_DIR and location of the R include directory using -DCMAKE_R_INC_DIR. For example

cmake -DCMAKE_R_INC_DIR="/usr/share/R/include" -DCMAKE_R_LIB_DIR="/usr/lib/R/lib/libR.so"  ../

To compile tests set -DBUILD_TESTS=ON. To compile example C/C++ application set -DBUILD_APPLICATIONS=ON.

The software can then be compiled using the generated Makefile. To do so, simply type

make

To compile tests use -DBUILD_TESTS=ON which compiles test executable performing a series of tests of supported RQA metrics.

An example application that uses the AccRQA library can be compiled with a flag -DBUILD_APPLICATIONS=ON.

Installation of C library (Windows)

To compile the AccRQA library from source you will require:

• MSVC compiler (part of Visual Studio)
• Make for Windows

In command line you can build AccRQA by

mkdir build
cd build
cmake .. [OPTIONS]
cmake --build .
cmake --install .

Contributors

• Karel Adamek
• Jan Novotny
• Radim Panis
• Norbert Marwan