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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 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]
Anaconda is a free and open-source system installer for Linux distributions.. Anaconda is used by Red Hat Enterprise Linux, Oracle Linux, Scientific Linux, Rocky Linux, AlmaLinux, CentOS, MIRACLE LINUX, Qubes OS, Fedora, Sabayon Linux and BLAG Linux and GNU, also in some less known and discontinued distros like Progeny Componentized Linux, Asianux, Foresight Linux, Rpath Linux and VidaLinux.
The CUDA platform is accessible to software developers through CUDA-accelerated libraries, compiler directives such as OpenACC, and extensions to industry-standard programming languages including C, C++, Fortran and Python. C/C++ programmers can use 'CUDA C/C++', compiled to PTX with nvcc, Nvidia's LLVM-based C/C++ compiler, or by clang itself. [9]
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 .
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.
pip (also known by Python 3's alias pip3) is a package-management system written in Python and is used to install and manage software packages. [4] The Python Software Foundation recommends using pip for installing Python applications and its dependencies during deployment. [5]
An online edition of the Ubuntu Software Center was released, the Ubuntu Apps Directory. The Web store shows the same content as the Software Center application, with a download button that opens the application if running Ubuntu or a link to download the Ubuntu operating system installer if running a different operating system. [9]