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developer.nvidia.com /cuda-zone 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.
hashcat - CPU-based password recovery tool; oclHashcat/cudaHashcat - GPU-accelerated tool (OpenCL or CUDA) With the release of hashcat v3.00, the GPU and CPU tools were merged into a single tool called hashcat. The CPU-only version became hashcat-legacy. [5] Both CPU and GPU now require OpenCL.
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.
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).
Nvidia launched CUDA in 2006, a software development kit (SDK) and application programming interface (API) that allows using the programming language C to code algorithms for execution on GeForce 8 series and later GPUs. ROCm, launched in 2016, is AMD's open-source response to CUDA. It is, as of 2022, on par with CUDA with regards to features ...
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).
Nvidia's Tegra K1 (codenamed "Logan") features ARM Cortex-A15 cores in a 4+1 configuration similar to Tegra 4, or Nvidia's 64-bit Project Denver dual-core processor as well as a Kepler graphics processing unit with support for Direct3D 12, OpenGL ES 3.1, CUDA 6.5, OpenGL 4.4/OpenGL 4.5, and Vulkan.
Nvidia outsources the manufacturing of the hardware it designs. [7] Nvidia's professional line of GPUs are used for edge-to-cloud computing and in supercomputers and workstations for applications in fields such as architecture, engineering and construction, media and entertainment, automotive, scientific research, and manufacturing design. [8]