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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.
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 unified shader model uses the same hardware resources for both vertex and fragment processing. In the field of 3D computer graphics, the unified shader model (known in Direct3D 10 as "Shader Model 4.0") refers to a form of shader hardware in a graphical processing unit (GPU) where all of the shader stages in the rendering pipeline (geometry, vertex, pixel, etc.) have the same capabilities.
64 CUDA cores [8] 16 raster operation (ROP) units, 32 texture address (TA) / texture filter (TF) units; 20.8 Gtexels/s fill rate; 650 MHz core clock, with a 1625 MHz unified shader clock; 1008 MHz memory (2016 MHz datarate), 256-bit interface for 64.5 GB/s of bandwidth. (57.6 GB/s for 1800 MHz configuration) 512–2048 MB of GDDR3 or DDR2 memory
Specifications of Intel Gen5 graphics processing units [22] [23] Name Launch Market Processor Device ID Execution units Core clock Memory API support Intel Clear Video HD; Code name Model DVMT Bandwidth Direct3D OpenGL OpenCL; HD Graphics 2010 Desktop Ironlake Celeron G1101 0042 12 533 1720 17 10.1 FL10_0 2.1 ES 2.0 Linux: No No Core i3-5x0
This is accelerated by the use of new RT (ray-tracing) cores, which are designed to process quadtrees and spherical hierarchies, and speed up collision tests with individual triangles. Features in Turing: CUDA cores (SM, Streaming Multiprocessor) Compute Capability 7.5; traditional rasterized shaders and compute
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.
The layout of SMM units is partitioned so that each of the 4 warp schedulers in an SMM controls 1 set of 32 FP32 CUDA cores, 1 set of 8 load/store units, and 1 set of 8 special function units. This is in contrast to Kepler, where each SMX has 4 schedulers that schedule to a shared pool of 6 sets of 32 FP32 CUDA cores, 2 sets of 16 load/store ...