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Vulkan targets high-performance real-time 3D-graphics applications, such as video games and interactive media, and highly parallelized computing. Vulkan is intended to offer higher performance and more efficient CPU and GPU usage compared to the older OpenGL and Direct3D 11 APIs. It does so by providing a considerably lower-level API for the ...
When it was first introduced, the name was an acronym for Compute Unified Device Architecture, [4] but Nvidia later dropped the common use of the acronym and now rarely expands it. [5] CUDA is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements for the execution of compute kernels. [6]
Nvidia GameWorks is a middleware software suite developed by Nvidia. [1] The Visual FX, PhysX, and Optix SDKs provide a wide range of enhancements pre-optimized for Nvidia GPUs . [ 2 ] GameWorks is partially open-source . [ 3 ]
Nvidia NVENC (short for Nvidia Encoder) [1] is a feature in Nvidia graphics cards that performs video encoding, offloading this compute-intensive task from the CPU to a dedicated part of the GPU. It was introduced with the Kepler -based GeForce 600 series in March 2012 (GT 610, GT620 and GT630 is Fermi Architecture).
Mesa Software Driver VIRGL starts Vulkan Development in 2018 with GSOC projects for support of Virtual machines. [108] Lavapipe is a CPU-based Software Vulkan driver and the brother of LLVMpipe. Mesa Version 21.1 supports Vulkan 1.1+. [109] Google introduces Venus Vulkan Driver for virtual machines in Mesa 21.1 with full support for Vulkan 1.2 ...
The negative implication for Nvidia is that by innovating at the software level as DeepSeek has done, AI companies may become less dependent on hardware, which could affect Nvidia's sales growth ...
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] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU.