| Moreover, CDNA is not a new architecture, but just a rebranding of GCN. CDNA 1 had little changes over the previous GCN variant, except for the addition of matrix operations, which have double throughput compared to the vector operations, like NVIDIA did before (the so-called "tensor" cores of NVIDIA GPUs). CDNA 2 had more important changes, with the double-precision operations becoming the main operations around which the compute units are structured, but the overall structure of the compute units has remained the same as in the first GCN GPUs from 2012. The changes made in RDNA vs. GCN/CDNA would have been as useful in scientific computing applications as they are in the gaming GPUs and RDNA is also defined to potentially have fast double-precision operations, even if no such RDNA GPU has been designed yet. I suppose that the reason why AMD has continued with GCN for the datacenter GPUs was their weakness in software development. Until today ROCm and the other AMD libraries and software tools for GPU computational applications have good support only for GCN/CDNA GPUs, while the support for RDNA GPUs was non-existent in the beginning and very feeble now. So I assume that they have kept GCN rebranded as CDNA for datacenter applications because they were not ready to develop appropriate software tools for RDNA. |
That's basically the reason for the split. Video game shaders need the latency improvements from RDNA (particularly the cache, but also the pipeline level latency improvements, each clock an instruction completed rather than once every 4 clocks like GCN).
But supercomputers care more about bandwidth. The once every 4 clocks on GCN/CDNA is far denser and more power efficient.