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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.
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.
The Nvidia CUDA Compiler (NVCC) translates code written in CUDA, a C++-like language, into PTX instructions (an assembly language represented as American Standard Code for Information Interchange text), and the graphics driver contains a compiler which translates PTX instructions into executable binary code, [2] which can run on the processing ...
Nvidia GPUs are used in deep learning, and accelerated analytics due to Nvidia's CUDA software platform and API which allows programmers to utilize the higher number of cores present in GPUs to parallelize BLAS operations which are extensively used in machine learning algorithms. [13]
Model – The marketing name for the processor, assigned by Nvidia. Launch – Date of release for the processor. Code name – The internal engineering codename for the processor (typically designated by an NVXY name and later GXY where X is the series number and Y is the schedule of the project for that generation).
Admittedly, AMD's ROCm is far behind Nvidia's CUDA, a software platform that helps developers speed up application development. Such an advantage continues to reinforce Nvidia's competitive lead.
As a result, CUDA became the de facto software on which developers learned to program GPUs, while in the years since the program was created, it has furthered its lead through CUDA X, a collection ...
The Ada Lovelace architecture follows on from the Ampere architecture that was released in 2020. The Ada Lovelace architecture was announced by Nvidia CEO Jensen Huang during a GTC 2022 keynote on September 20, 2022 with the architecture powering Nvidia's GPUs for gaming, workstations and datacenters.