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

• GCC (GNU Compiler Collection)
  1. Domain: Compiler
  2. 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.

• LLVM
  1. Domain: Compiler Infrastructure
  2. 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.

• CUDA
  1. Domain: GPU Computing
  2. 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.

• cuDNN
  1. Domain: Deep Learning
  2. 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.

• PyTorch
  1. Domain: Machine Learning
  2. 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.

• TensorFlow
  1. Domain: Machine Learning
  2. 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.

• GSL (GNU Scientific Library)
  1. Domain: Mathematics & Numerical Computation
  2. Description: GSL is a collection of numerical routines for scientific computing, including statistics, linear algebra, and numerical integration.

• FFTW
  1. Domain: Signal Processing
  2. 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.

• HDF5
  1. Domain: Data Storage
  2. 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.

• NumPy
  1. Domain: Scientific Computing & Data Science
  2. 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.

• SciPy
  1. Domain: Scientific Computing
  2. 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.

• LAPACK
  1. Domain: Linear Algebra
  2. 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.

• OpenBLAS
  1. Domain: Linear Algebra
  2. 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.

• MAGMA
  1. Domain: Linear Algebra & HPC
  2. 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.

• MPICH
  1. Domain: Parallel Computing
  2. Description: MPICH is an implementation of the Message Passing Interface (MPI) standard for parallel computing. It is widely used in high-performance parallel applications.

• OpenMPI
  1. Domain: Parallel Computing
  2. 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.

• Pandas
  1. Domain: Data Science
  2. 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.

• Python
  1. Domain: Programming Language
  2. Description: Python is a versatile, high-level programming language that is widely used in web development, data science, automation, machine learning, and more.