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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.
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.
Anaconda is an open source [9] [10] data science and artificial intelligence distribution platform for Python and R programming languages.Developed by Anaconda, Inc., [11] an American company [1] founded in 2012, [11] the platform is used to develop and manage data science and AI projects. [9]
The Nvidia CUDA Compiler (NVCC) translates code written in CUDA, a C++-like language, into PTX instructions (an assembly language), and the graphics driver contains a compiler which translates PTX instructions into executable binary code, [2] which can run on the processing cores of Nvidia graphics processing units (GPUs).
In computing, CUDA is a proprietary [2] parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for accelerated general-purpose processing, an approach called general-purpose computing on GPUs.
Conda is an open-source, [2] cross-platform, [3] language-agnostic package manager and environment management system. 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 .
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]
It is an open-source cross-platform integrated development environment (IDE) for scientific programming in the Python language.Spyder integrates with a number of prominent packages in the scientific Python stack, including NumPy, SciPy, Matplotlib, pandas, IPython, SymPy and Cython, as well as other open-source software.