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NVWMI – NVIDIA Enterprise Management Toolkit; GameWorks PhysX – is a multi-platform game physics engine; CUDA 9.0–9.2 comes with these other components: CUTLASS 1.0 – custom linear algebra algorithms, NVIDIA Video Decoder was deprecated in CUDA 9.2; it is now available in NVIDIA Video Codec SDK; CUDA 10 comes with these other components:
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.
CUDA is the toolkit used by developers to build large language models and maximize the computing potential of their Nvidia GPUs. It ensures that enterprise clients stay within its ecosystem of ...
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. It is accompanied by NVENC for video encoding in Nvidia's Video Codec SDK. [2]
Nvidia's CUDA software platform has played a key role in its success, too. CUDA is the toolkit developers use to build LLMs as well as get as much computing capacity out of their GPUs as possible.
Nvidia OptiX (OptiX Application Acceleration Engine) is a ray tracing API that was first developed around 2009. [1] The computations are offloaded to the GPUs through either the low-level or the high-level API introduced with CUDA. CUDA is only available for Nvidia's graphics products. Nvidia OptiX is part of Nvidia GameWorks. OptiX is a high ...
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]
Nvidia's CUDA is closed-source, whereas AMD ROCm is open source. There is open-source software built on top of the closed-source CUDA, for instance RAPIDS. CUDA is able run on consumer GPUs, whereas ROCm support is mostly offered for professional hardware such as AMD Instinct and AMD Radeon Pro.