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.

• 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.