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| hardware [2025/04/08 01:34] – nshegunov | hardware [2026/01/08 23:35] (current) – removed nshegunov |
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| ====== Popular Libraries for Scientific Computing, Machine Learning, and Data Science ====== | |
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| 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. | |
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| ===== 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. | |
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| * **[[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. | |
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| ===== 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. | |
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| * **[[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. | |
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| * **[[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. | |
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| * **[[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. | |
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| ===== 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. | |
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| * **[[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. | |
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| * **[[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. | |
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| * **[[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. | |
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| * **[[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. | |
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| * **[[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. | |
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| * **[[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. | |
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| ===== 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. | |
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| * **[[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. | |
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| * **[[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. | |
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| ===== 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. | |
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| * **[[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. | |
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