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CuPy is a part of the NumPy ecosystem array libraries [7] and is widely adopted to utilize GPU with Python, [8] especially in high-performance computing environments such as Summit, [9] Perlmutter, [10] EULER, [11] and ABCI.
It was originally developed to solve package management challenges faced by Python data scientists, and today is a popular package manager for Python and R. [4] [5] At first, Anaconda Python distribution was developed by Anaconda Inc.; later, it was spun out as a separate package, [6] released under the BSD license.
Pip's command-line interface allows the install of Python software packages by issuing a command: pip install some-package-name. Users can also remove the package by issuing a command: pip uninstall some-package-name. pip has a feature to manage full lists of packages and corresponding version numbers, possible through a "requirements" file. [14]
CUDA provides both a low level API (CUDA Driver API, non single-source) and a higher level API (CUDA Runtime API, single-source). The initial CUDA SDK was made public on 15 February 2007, for Microsoft Windows and Linux. Mac OS X support was later added in version 2.0, [17] which supersedes the beta released February 14, 2008. [18]
Linux Mint began in 2006 with a beta release, 1.0, code-named 'Ada', [13] based on Kubuntu and using its KDE interface. Linux Mint 2.0 'Barbara' was the first version to use Ubuntu as its codebase and its GNOME interface. It had few users until the release of Linux Mint 3.0, 'Cassandra'.
The 2.x versions of YUM feature an additional interface for programming extensions in Python that allows the behavior of YUM to be altered. Certain plug-ins are installed by default. [26] A commonly installed [27] package yum-utils, contains commands which use the YUM API, and many plugins.
CUDA code runs on both the central processing unit (CPU) and graphics processing unit (GPU). NVCC separates these two parts and sends host code (the part of code which will be run on the CPU) to a C compiler like GNU Compiler Collection (GCC) or Intel C++ Compiler (ICC) or Microsoft Visual C++ Compiler, and sends the device code (the part which will run on the GPU) to the GPU.
rCUDA, which stands for Remote CUDA, is a type of middleware software framework for remote GPU virtualization. Fully compatible with the CUDA application programming interface ( API ), it allows the allocation of one or more CUDA-enabled GPUs to a single application.