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the benchmarks are not about GEMM, but real-world deep learning workloads which could have very different characteristics from GEMM

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I just wanted to caution that one has to be careful what one is comparing against, as the libraries got significant speed improvements over time, without that being widely advertised. So it matters a lot if one compares this library against CuDNN from 1 month ago, or to CuDNN from 2 years ago. The latter is _much_ slower.

The GEMM example was just there as the details of the optimization have been published, unlike most other hand-tuned assembler routines for DNN workloads.

CuDNN v7 was used in the experiments, in the experiments parts each comparison was listed with version or commit number.

Well, I didn't RTFA. This was not meant to be specific for this article though.

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