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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.
In computing, CUDA (Compute Unified Device Architecture) 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.
This is a list of POSIX (Portable Operating System Interface) commands as specified by IEEE Std 1003.1-2024, which is part of the Single UNIX Specification (SUS). These commands can be found on Unix operating systems and most Unix-like operating systems.
The wsl.exe command accesses and manages Linux distributions in WSL via command-line interface (CLI) – for example via Command Prompt or PowerShell. With no arguments it enters the default distribution shell. It can list available distributions, set a default distribution, and uninstall distributions. [31]
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).
Nvidia NVDEC (formerly known as NVCUVID [1]) is a feature in its graphics cards that performs video decoding, offloading this compute-intensive task from the CPU. [2] NVDEC is a successor of PureVideo and is available in Kepler and later Nvidia GPUs.
DPC++ [8] [9] is a programming language implementation of oneAPI, built upon the ISO C++ and Khronos Group SYCL standards. [10] DPC++ is an implementation of SYCL with extensions that are proposed for inclusion in future revisions of the SYCL standard, including: unified shared memory, group algorithms, and sub-groups.
Train with Parallel Computing Toolbox and generate CUDA code with GPU Coder [23] No Yes [24] Yes [25] [26] Yes [25] Yes [25] Yes With Parallel Computing Toolbox [27] Yes Microsoft Cognitive Toolkit (CNTK) Microsoft Research: 2016 MIT license [28] Yes Windows, Linux [29] (macOS via Docker on roadmap) C++