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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.
The Nvidia CUDA Compiler (NVCC) translates code written in CUDA, a C++-like language, into PTX instructions (an IL), 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).
The distributed-computing project Folding@home uses scientific computer programs, referred to as "cores" or "fahcores", to perform calculations. [1] [2] Folding@home's cores are based on modified and optimized versions of molecular simulation programs for calculation, including TINKER, GROMACS, AMBER, CPMD, SHARPEN, ProtoMol, and Desmond.
Core config – The layout of the graphics pipeline, in terms of functional units. Over time the number, type, and variety of functional units in the GPU core has changed significantly; before each section in the list there is an explanation as to what functional units are present in each generation of processors.
CUDA is a parallel computing platform and programming model that higher level languages can use to exploit parallelism. In CUDA, the kernel is executed with the aid of threads. The thread is an abstract entity that represents the execution of the kernel. A kernel is a function that compiles to run on a special device. Multi threaded ...
Painting of Blaise Pascal, eponym of architecture. Pascal is the codename for a GPU microarchitecture developed by Nvidia, as the successor to the Maxwell architecture. The architecture was first introduced in April 2016 with the release of the Tesla P100 (GP100) on April 5, 2016, and is primarily used in the GeForce 10 series, starting with the GeForce GTX 1080 and GTX 1070 (both using the ...
A GPU cluster is a computer cluster in which each node is equipped with a graphics processing unit (GPU). By harnessing the computational power of modern GPUs via general-purpose computing on graphics processing units (GPGPU), very fast calculations can be performed with a GPU cluster.
Note that the previous generation Tesla could dual-issue MAD+MUL to CUDA cores and SFUs in parallel, but Fermi lost this ability as it can only issue 32 instructions per cycle per SM which keeps just its 32 CUDA cores fully utilized. [3] Therefore, it is not possible to leverage the SFUs to reach more than 2 operations per CUDA core per cycle.