Quadro pascal fp64 series#

Since instructions can be executed at the same time along these different paths (Concurrent Execution), effective floating point throughput is boosted by 36 percent (using Nvidia’s analysis). In Turing, Nvidia has created a completely separate and independent datapath for integer math by including an integer core (INT32) for every standard floating point CUDA (FP32) core. Previous SM designs were forced to run both types along the same datapath, meaning that integer instructions halted the queue of floating point ones. In an analysis of ‘several modern applications’ (gaming), Nvidia claims to have found that for every 100 floating point instructions (the most common in graphics shader workloads), there would be on average 36 simpler integer instructions that also needed computing.

Quadro pascal fp64 series#

The GPUs are still divided into Graphics Processing Clusters (GPCs) which sport a Raster Engine linked to a series of Texture Processing Clusters (TPCs), inside which are the SMs, and that’s where the differences start. There are high-level similarities to Pascal GPUs that up-to-date gamers will be familiar with. Excluding AI and ray tracing workloads, the push with Turing is on performance more than it is efficiency, and at the heart of improved performance is a redesign of the Streaming Multiprocessor (SM). All three are fabricated on the 12nm FFN (FinFET Nvidia) process from TSMC, although density is roughly on par with the 16nm Pascal chips. As usual, the building blocks that we’ll move through are the same in each, so they all share the same benefits and features of the new design.

Only the RTX 2070 features a fully enabled part the RTX 2080 Ti and RTX 2080 have a few bits disabled, with fully enabled GPUs reserved for Quadro cards. Nvidia has thus far announced three separate Turing GPUs: The current flagship TU102 (shown below), which features in the RTX 2080 Ti TU104, found in the RTX 2080 and TU106, which comes in the RTX 2070.