====== Popular Libraries for Scientific Computing, Machine Learning, and Data Science ======

This document provides an overview of widely used libraries across various domains such as scientific computing, machine learning, data science, and high-performance computing, available on the unite cluster. 

----

===== Compiler Libraries =====
  * **[[https://gcc.gnu.org | GCC (GNU Compiler Collection)]]**
    - Domain: Compiler
    - Description: GCC is a collection of compilers for various programming languages, including C, C++, and Fortran. It's essential for compiling code in high-performance computing and software development.

  * **[[https://llvm.org | LLVM]]**
    - Domain: Compiler Infrastructure
    - Description: LLVM is a collection of modular and reusable compiler and toolchain technologies. It’s used for developing compilers, optimizers, and other code analysis tools.

===== GPU Computing & Deep Learning =====
  * **[[https://developer.nvidia.com/cuda-zone | CUDA]]**
    - Domain: GPU Computing
    - Description: CUDA is a parallel computing platform and application programming interface model created by NVIDIA. It allows developers to utilize GPUs for general-purpose processing.

  * **[[https://developer.nvidia.com/cudnn | cuDNN]]**
    - Domain: Deep Learning
    - Description: cuDNN is a GPU-accelerated library for deep neural networks. It's optimized for high performance on NVIDIA hardware and is widely used in machine learning and AI applications.

  * **[[https://pytorch.org | PyTorch]]**
    - Domain: Machine Learning
    - Description: PyTorch is an open-source machine learning library based on the Torch library. It is widely used in deep learning and research due to its flexibility and ease of use.

  * **[[https://www.tensorflow.org | TensorFlow]]**
    - Domain: Machine Learning
    - Description: TensorFlow is an open-source platform for machine learning. It provides a comprehensive ecosystem of tools, libraries, and community resources to facilitate the development of ML models.

===== Scientific Computing & Numerical Libraries =====
  * **[[https://www.gnu.org/software/gsl | GSL (GNU Scientific Library)]]**
    - Domain: Mathematics & Numerical Computation
    - Description: GSL is a collection of numerical routines for scientific computing, including statistics, linear algebra, and numerical integration.

  * **[[http://www.fftw.org | FFTW]]**
    - Domain: Signal Processing
    - Description: FFTW is a C library for computing discrete Fourier transforms (DFT). It is highly efficient and widely used for signal processing and numerical simulations.

  * **[[https://www.hdfgroup.org/solutions/hdf5 | HDF5]]**
    - Domain: Data Storage
    - Description: HDF5 is a file format and set of tools for storing and managing large amounts of data. It's commonly used in scientific computing for high-volume data storage.

  * **[[https://numpy.org | NumPy]]**
    - Domain: Scientific Computing & Data Science
    - Description: NumPy is the fundamental package for numerical computing in Python. It provides support for large multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.

  * **[[https://scipy.org | SciPy]]**
    - Domain: Scientific Computing
    - Description: SciPy is a Python library used for scientific and technical computing. It builds on NumPy and provides a collection of algorithms for optimization, integration, interpolation, eigenvalue problems, and more.

  * **[[http://www.netlib.org/lapack | LAPACK]]**
    - Domain: Linear Algebra
    - Description: LAPACK is a library for solving linear algebra problems such as systems of linear equations, least squares problems, eigenvalue problems, and singular value decomposition.

  * **[[https://www.openblas.net | OpenBLAS]]**
    - Domain: Linear Algebra
    - Description: OpenBLAS is an optimized BLAS (Basic Linear Algebra Subprograms) library that provides highly optimized matrix operations and is used for high-performance numerical computing.

===== High-Performance Computing (HPC) =====
  * **[[http://icl.cs.utk.edu/magma | MAGMA]]**
    - Domain: Linear Algebra & HPC
    - Description: MAGMA is a library for high-performance linear algebra routines on multi-core and multi-GPU systems. It's a key tool in high-performance computing (HPC) applications.

  * **[[https://www.mpich.org | MPICH]]**
    - Domain: Parallel Computing
    - Description: MPICH is an implementation of the Message Passing Interface (MPI) standard for parallel computing. It is widely used in high-performance parallel applications.

  * **[[https://www.open-mpi.org | OpenMPI]]**
    - Domain: Parallel Computing
    - Description: OpenMPI is an open-source implementation of the Message Passing Interface (MPI) for parallel and distributed computing. It is used in high-performance computing environments for multi-node computations.

===== Data Science & Data Manipulation =====
  * **[[https://pandas.pydata.org | Pandas]]**
    - Domain: Data Science
    - Description: Pandas is a fast, powerful, flexible, and easy-to-use data analysis and manipulation library for Python. It’s used extensively in data science and data analysis.

  * **[[https://www.python.org | Python]]**
    - Domain: Programming Language
    - Description: Python is a versatile, high-level programming language that is widely used in web development, data science, automation, machine learning, and more.