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CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3]
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]
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.
Linux System software package for correlated tracing of kernel, applications and libraries. GPL/LGPL/MIT OProfile: Linux Profiles everything running on the Linux system, including hard-to-profile programs such as interrupt handlers and the kernel itself. Sampling profiler for Linux that counts cache misses, stalls, memory fetches, etc.
Programmable, direct support of 2D+3D plotting. Interfaces to many other software packages. Interfacing to external modules written in C, Java, Python or other languages. Language syntax similar to MATLAB. Used for numerical computing in engineering and physics. Smath Studio: SMath LLC (Andrey Ivashov) 2006 1.0.8348 11 September 2022: Free
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.
Numba is an open-source JIT compiler that translates a subset of Python and NumPy into fast machine code using LLVM, via the llvmlite Python package.It offers a range of options for parallelising Python code for CPUs and GPUs, often with only minor code changes.